Nt1330 Unit 1 Assignment
Acknowledgement
The satisfaction and euphoria that accompany the successful completion of any task would be incomplete without the people who made it possible, whose constant guidance and encouragement crowned our efforts with success.
My primary thanks goes to Almighty Allah by whose Blessing I have completed my report in the given time. I would like to thank Prof. Zain Ul Abideen Valika for his patience, guidance and encouragement in successful completion of this report .
Finally, I would like to immensely thank my parents and all my friends for their constant support and encouragement which was the fuel propelling my effort.
Abstract
In this Hi-Tech era, there is a great demand to identify and authenticate the individuals. Till now we are totally dependent upon Passwords and Pin Numbers for identification. How secure are passwords? With the numerous passwords that an individual has to remember, they are often forgotten, …show more content…
misplaced, or stolen. Think of how many different passwords you have to remember: computer passwords, internet site logons and passwords, PIN numbers for the ATM and for credit cards, the list goes on. We are arriving at a conclusion that these technologies are not sufficient for the security of an individual as these are hard to remember, easily transferable, easily stolen and there are many weaknesses. Due to these weaknesses biometrics came into existence.
Biometrics is that study of science that deals with personal human behavioral and physiological characteristics and such as fingerprints, handprints, iris scanning, voice scanning, face recognition and signature recognition. These technologies are far more promising than that which are used currently to identify an individual. This paper highlights some of the benefits and the few limitations of using biometrics for authentication .With biometrics it doesn’t matter if we forget your password or lose your smartcard.
Contents
Introduction
The term "biometrics" is derived from the Greek words bio means “life” and metric means “to measure”. BIOMETRICS refers to the automatic identification of a person based on his physiological / behavioral characteristics. This method of identification is preferred for various reasons; the person to be identified is required to be physically present at the point of identification; identification based on biometric techniques obviates the need to remember a password or carry a token.
A biometric is a unique, measurable characteristic or trait for automatically recognizing or verifying the identity of a human being. Biometrics is a powerful combination of science and technology that can be used to protect and secure our most valuable information and property.
Recognition requires the system to look through many stored sets of characteristics and pick the one that matches the unknown individual being presented. Various types of biometric systems are being used for real–time identification; the most popular are based on face recognition and fingerprint matching. However there are other biometric systems that utilize iris and retinal scan, speech, gesture recognition , and hand geometry. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. The basic idea behind biometrics is that our bodies contain unique properties that can be used to distinguish us from others. A biometric system is essentially a pattern recognition system, which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristics possessed by the user. An important issue in designing a practical system is to determine how an individual is identified. Depending on the context, a biometric system can be either a verification (authentication) system an identification system. Verification involves confirming or denying a person’s claimed identity. In identification one has to establish a person’s identity. Identification systems based on biometrics are important building blocks in simplifying our interaction with the myriad digital systems and devices that we are all using in increasing numbers. There are levels of security from the most basic to the most robust with biometrics being the most secure:
Something that you have- such as an ID badge with a photograph on it.
Something that you know- such as a password or PIN number.
Something which you are- such as biometric data – fingerprints, iris, voice or face scans.
Biometrics is rapidly evolving technology, which is being used in forensics such as criminal identification and prison security, and has the potential to be used in a large range of civilian application areas. Biometrics can be used transactions conducted via telephone and Internet (electronic commerce and electronic banking). In automobiles, biometrics can replace keys with key -less entry devices.
How Biometric System Works
Biometric devices consist of a reader or scanning device, software that converts the gathered information into digital form, and a database that stores the biometric data for comparison with previous records.
When converting the biometric input, the software identifies specific points of data as match points. The match points are processed using an algorithm into a value that can be compared with biometric data in the database. The biometric feature must have the following characteristics:-(a) Universality, which means that every person should have the characteristic,(b) Uniqueness, two persons should not have the same term or measurement of Characteristic.(c) Permanence, the characteristic should be invariant with time.(d) Measurability, the characteristic can be quantified that is the origin of the Cameras used in biometric systems are generally either CCD (charge couple device) or CMOS(combined metal oxide semiconductor) image sensors. CCD is comparatively more costly than
CMOS.
The main operations a system can perform are enrollment and test. During the enrollment, biometric information from an individual is stored. During the test, biometric information is detected and compared with the stored information. Note that it is crucial that storage and retrieval of such systems themselves be secure if the biometric system is, robust.
The first block (sensor) is the interface between the real world and the system; it has to acquire all the necessary data. Most of the times it is an image acquisition system, but it can change according to the characteristics desired. A sample of the biometric trait is captured, processed by a computer, and stored for later comparison.
The second block performs all the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization, etc.
In the third block features needed are extracted. This step is an important step as the correct features need to be extracted and the optimal way. A vector of numbers or an image with particular properties is used to create a template. A template is syntheses of all the characteristics extracted from the source, in the optimal size to allow for adequate identify ability. All Biometric authentications require comparing a registered or enrolled biometric sample (biometric template or identifier) against a newly captured biometric sample.
If enrollment is being performed where the biometric system identifies a person from the entire enrolled population by searching a database for a match based solely on the biometric. For example, an entire database can be searched to verify a person has not applied for entitlement benefits under two different names. This is sometimes called “one-to-many” matching.
If a verification phase is being performed, the biometric system authenticates a person’s claimed identity from their previously enrolled pattern. This is also called “one-to-one” matching. The obtained template is passed to a matcher that compares it with other existing templates. The matching program will analyze the template with the input. This will then be output for any specified use or purpose.
Classification of Biometrics
Biometrics encompasses both physiological and behavioral characteristics. A physiological characteristic are related to the shape of a body. A relatively stable physical feature such as finger print, hand geometry, iris pattern or facial features. These factors are basically unalterable without trauma to the individual. Behavioral tracts, on the other hand, are related to the behavior of a person. The most common trait used in identification is a person’s signature. Other behaviors used include a person’s keyboard typing, gait and speech patterns. Most of the behavioral characteristics change over time. Some of physical biometrics is
Fingerprint - analyzing fingertip patterns.
Facial Recognition- measuring facial characteristics
Hand Geometry- measuring the shape of the hand.
Iris recognition- analyzing features of colored ring of the eye.
Some of behavioral biometrics is
Speaker recognition- analyzing vocal behavior.
Signature recognition - analyze the physical activity of signing.
Gesture recognition - analyzing the motions of body
Humans have used fingerprints for personal identification for many centuries and the matching accuracy using fingerprints has been shown to be very high.
Finger printing
Finger printing is probably the best-known biometric- method of identification used for 100 years. There are a few variants of image capture technology available for such commercially oriented fingerprint sensor, including optical, silicon, ultrasound, thermal and hybrid. Among all the biometric techniques, fingerprint-based Identification is the oldest method that has been successfully used in numerous applications. Everyone is known to have unique, immutable fingerprints. A fingerprint is made of a series of ridges and furrows on the surface of the finger as shown in the fig 3.1.1. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as minutiae points. Minutiae points are the local ridge characteristics that occur either at a ridge ending or a ridge bifurcation. A ridge ending is defined as the point where the ridge ends abruptly and the ridge bifurcation is the point where the ridge splits into two or more branches. When a user places their finger on the terminals scanner the image is electronically read, analyzed, and compared with a previously recorded image of the same finger which has been stored in the database. The imaging process is based on digital holography, using an electro-optical scanner about the size of a thumbprint. The scanner reads three-dimensional data from the finger such as skin undulations, and ridges and valleys, to create a unique pattern that is composed into a template file.
An algorithm is developed to classify fingerprints into five classes, namely, whorl, right loop, arch and tented arch as shown in figure 3. Critical points in a finger print, called core and delta are marked on one of the fingers as shown in figure 3 (c). The cores the inner point, normally in the middle of the print, around which swirls, loops, or arches center. It is frequently characterized by a ridge ending and several acutely curved ridges. Deltas are the points, normally at the lower left and right hand of the fingerprint, around which a triangular series of ridges center. The algorithm separates the number of ridges present in four directions (o degree, 45 degree, 90 degree and 135 degree) by filtering the central part of a fingerprint with a bank of Gabor filters. This information is quantized to generate a finger code which is used for classification. To avoid fake-finger attacks, some systems employ so-called live detection technology, which takes advantage of the sweat activity of human bodies. High-magnification lenses and special illumination technologies capture the finger’s perspiration and pronounce the finger dead or alive. Advantages:-
Fingerprint recognition equipment is relatively low-priced compared to other biometric system.
Fingerprints are unique to each finger of each individual and the ridge arrangement remains permanent during one's lifetime.
Disadvantages:-
Some people have damaged or eliminated fingerprints.
Vulnerable to noise and distortion brought on by dirt and twists.
Handwriting
At first glance, using handwriting to identify people might not seem like a good idea. After all, many people can learn to copy other people's handwriting with a little time and practice. It seems like it would be easy to get a copy of someone's signature or the required password and learn to forge it.
But biometric systems don't just look at how you shape each letter; they analyze the act of writing. They examine the pressure you use and the speed and rhythm with which you write. They also record the sequence in which you form letters, like whether you add dots and crosses as you go or after you finish the word.
Unlike the simple shapes of the letters, these traits are very difficult to forge. Even if someone else got a copy of your signature and traced it, the system probably wouldn't accept their forgery.
A handwriting recognition system's sensors can include a touch-sensitive writing surface or a pen that contains sensors that detect angle, pressure and direction. The software translates the handwriting into a graph and recognizes the small changes in a person's handwriting from day to day and over time.
Hand and Finger Geometry
People's hands and fingers are unique -- but not as unique as other traits, like fingerprints or irises. That's why businesses and schools, rather than high-security facilities, typically use hand and finger geometry readers to authenticate users, not to identify them. Disney theme parks, for example, use finger geometry readers to grant ticket holders admittance to different parts of the park. Some businesses use hand geometry readers in place of timecards.
Systems that measure hand and finger geometry use a digital camera and light. To use one, you simply place your hand on a flat surface, aligning your fingers against several pegs to ensure an accurate reading. Then, a camera takes one or more pictures of your hand and the shadow it casts. It uses this information to determine the length, width, thickness and curvature of your hand or fingers. It translates that information into a numerical template.
Hand and finger geometry systems have a few strengths and weaknesses. Since hands and fingers are less distinctive than fingerprints or irises, some people are less likely to feel that the system invades their privacy. However, many people's hands change over time due to injury, changes in weight or arthritis. Some systems update the data to reflect minor changes from day to day.
For higher-security applications, biometric systems use more unique characteristics, like voices. We'll look at those next.
Advantages:-
Easy to capture.
The major advantage is that most people can use it and as such, the acceptance rate is good. Believed to be a highly stable pattern over the adult lifespan.
Disadvantages:-
Use requires some training. System requires a large amount of physical space.
Face recognition:- Face recognition technology analyze the unique shape, pattern and positioning of the facial features. Face recognition is very complex technology and is largely software based. Face recognition starts with a picture, attempting to find a person in the image. This can be accomplished using several methods including movement, skin tones, or blurred human shapes. The face recognition system locates the head and finally the eyes of the individual. A matrix is then developed based on the characteristics of the individual’s face. The method of defining the matrix varies according to the algorithm (the mathematical process used by the computer to perform the comparison). This matrix then compared to matrices that are in a database and a similarity score is generated for each comparison. Despite the fact that there are more reliable biometric recognition techniques such as fingerprint and iris recognition, these techniques are intrusive and their success depends highly on user cooperation, since the user must position her eye in front of their is scanner or put her finger in the fingerprint device. On the other hand, face recognition is non-intrusive since it is based on images recorded by a distant camera, and can be very effective even if the user is not aware of the existence of the face recognition system. The human face is undoubtedly the most common characteristic used by humans to recognize other people and this is why personal identification based on facial images is considered the friendliest among all biometrics. Face has certain distinguishable landmarks that are the peaks and valleys that sum up the different facial features. There are about 80 peaks and valleys on a human face. The following are a few of the peaks and valleys that are measured by the software
Distance between eyes
Width of nose
Depth of eye sockets
Cheekbones
Jaw line
Chin
These peaks and valleys are measured to give a numerical code, a string of numbers, which represents the face in a database. This code is called a face print. Face recognition involves the comparison of a given face with other faces in a database with the objective of deciding if the face matches any of the faces in that database.
1 1.Detection of the face in a complex background and localization of its exact position,
2 Extraction of facial features such as eyes, nose, etc., followed by normalization to align the face with the stored face images, and
3 Face classification or matching.
In addition, a face recognition system usually consists of the following four modules:
1 .Sensor module, which captures face images of an individual. Depending on the sensor modality, the acquisition device maybe a black and white or color camera, a 3D sensor capturing range (depth) data, or an infrared camera capturing infrared images.
2 Face detection and feature extraction module. The acquired face images are first scanned to detect the presence of faces and find their exact location and size. The output of face detection is an image window containing only the face area. Irrelevant information, such as background, hair, neck and shoulders, ears, etc. are discarded.
3 Classification module, in which the template extracted during step 2, is compared against the stored templates in the database to generate matching scores, which reveal how identical the faces in the probe and gallery images are. Then, a decision-making module either confirms (verification) or establishes (identification) the user’s identity based on the matching score. In case of face verification, the matching score is compared to a predefined threshold and based on the result of this comparison; the user is either accepted or rejected. In case of face identification, a set of matching scores between the extracted template and the templates of enrolled users is calculated. If the template of user X produces the best score, then the unknown face is more similar to X, than any other person in the database. To ensure that the unknown face is actually X and not an impostor, the matching score is compared to a predefined threshold.
4 Sometimes, more than one template per enrolled user is stored in the gallery database to account for different variations. Templates may also be updated over time, mainly to cope with variations due to aging.
Face detection algorithms can be divided into three categories according to
1 Knowledge-based methods are based on human knowledge of the typical human face geometry and facial features arrangement. Taking advantage of natural face symmetry and the natural top-to-bottom and left-to-right order in which features appear in the human face, these methods find rules to describe the shape, size, texture and other characteristics of facial features (such as eyes, nose, chin, eyebrows) and relationships between them (relative positions and distances). A hierarchical approach may be used, which examines the face at different resolution levels. At higher levels, possible face candidates are found using a rough description of face geometry. At lower levels, facial features are extracted and an image region is identified as face or non-face based on predefined rules about facial characteristics and their arrangement.
2 Feature invariant approaches aim to find structural features that exist even when the viewpoint or lighting conditions vary and then use these to locate faces. Different structural features are being used: facial local features, texture, and shape and skin color. Local features such as eyes, eyebrows, nose, and mouth are extracted using multi-resolution or derivative filters, edge detectors, morphological operations or thresholding. Statistical models are then built to describe their relationships and verify the existence of a face. Neural networks, graph matching, and decision trees were also proposed to verify face candidates.
3. Template-based methods. To detect a face in a new image, first the head outline, which is fairly consistently roughly elliptical, is detected using filters or edge detectors. Then the contours of local facial features are extracted in the same way, exploiting knowledge of face and feature geometry. More recently, techniques that rely on 3D shape data have been proposed. 3D face recognition is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. 3D face recognition has the potential to achieve better accuracy than its 2D counterpart by measuring geometry of rigid features on the face. This avoids such pitfalls of 2D face recognition algorithms as change in lighting, different facial expressions, make-up and head orientation.
Advantages:-
No contact required.
Commonly available sensors (cameras).
Disadvantages:-
Face can be obstructed by hair, glasses, hats, scarves etc.
Difficult to distinguish between twins.
Sensitive to changes in lighting, expression, and poses faces changeover time.
Iris Recognition:-
Iris scanning can seem very futuristic, but at the heart of the system is a simple CCD digital camera. It uses both visible and near-infrared light to take a clear, high-contrast picture of a person's iris. With near-infrared light, a person's pupil is very black, making it easy for the computer to isolate the pupil and iris.
When you look into an iris scanner, either the camera focuses automatically or you use a mirror or audible feedback from the system to make sure that you are positioned correctly. Usually, your eye is 3 to 10 inches from the camera. When the camera takes a picture, the computer locates:
The center of the pupil
The edge of the pupil
The edge of the iris
The eyelids and eyelashes
It then analyzes the patterns in the iris and translates them into a code.
An iris scanner
Iris scanners are becoming more common in high-security applications because people's eyes are so unique (the chance of mistaking one iris code for another is 1 in 10 to the 78th power [ref]. They also allow more than 200 points of reference for comparison, as opposed to 60 or 70 points in fingerprints.
The iris is a visible but protected structure, and it does not usually change over time, making it ideal for biometric identification. Most of the time, people's eyes also remain unchanged after eye surgery, and blind people can use iris scanners as long as their eyes have irises. Eyeglasses and contact lenses typically do not interfere or cause inaccurate readings.
Advantages:-
Iris recognition is very accurate with very low false acceptance rate
Disadvantages:-
Complex procedure.
High cost.
Speaker Recognition
Speaker, or voice, recognition is a biometric modality that uses an individual’s voice for recognition purposes. The speaker recognition process relies on features influenced by both the physical structure of an individual’s vocal tract and the behavioral characteristics of the individual. A popular choice for remote authentication due to the availability of devices for collecting speech samples and its ease of integration, speaker recognition is different from some other biometric methods in that speech samples are captured dynamically or over a period of time, such as a few seconds. Analysis occurs on a model in which changes over time are monitored.
Voice recognition technology utilizes the distinctive aspects of the voice to verify the identity of individuals. Voice recognition is occasionally confused with speech recognition, a technology which translates what a user is saying (a process unrelated to authentication). Voice recognition technology, by contrast, verifies the identity of the individual who is speaking. The two technologies are often bundled – speech recognition is used to translate the spoken word into an account number, and voice recognition verifies the vocal characteristics against those associated with this account.
Voice recognition can utilize any audio capture device, including mobile and land telephones and PC microphones. The performance of voice recognition systems can vary according to the quality of the audio signal as well as variation between enrollment and verification devices, so acquisition normally takes place on a device likely to be used for future verification. During enrollment an individual is prompted to select a passphrase or to repeat a sequence of numbers. Voice recognition can function as a reliable authentication mechanism for automated telephone systems, adding security to automated telephone-based transactions in areas such as financial services and health care. Certain voice recognition technologies are highly resistant to imposter attacks, means that voice recognition can be used to protect reasonably high-value transactions. samples are waveforms with time on the horizontal axis and loudness on the vertical access. The speaker recognition system analyzes the frequency content of the speech and compares characteristics such as the quality, duration, intensity dynamics, and pitch of the signal.
Voice recognition techniques can be divided into categories depending on the type of authentication domain.
Fixed text method is a technique where the speaker is required to say a predetermined word that is recorded during registration on the system.
In the text dependent method the system prompts the user to say a specific word or phrase, which is then computed on the basis of the user’s fundamental voice pattern.
The text independent method is an advanced technique where the user need not articulate any specific word or phrase. The matching is done by the system on the basis of the fundamental voice patterns irrespective of the language and the text used.
Advantages:-
Simple and cost-effective technological application.
Can be used for remote authentication.
Disadvantages:-
Voice and language usage change over time (e.g. as a result of age or illness).
Signature Recognition:-
Biometric signature recognition systems measure and analyze the physical activity of signing. Important characteristics include stroke order, the pressure applied, the pen-up movements, the angle the pen is held, the time taken to sign, the velocity and acceleration of the signature. Some systems additionally compare the visual image of signatures, though the focus in signature biometrics lies on writer-specific information rather than visual handwritten content. While it may appear trivial to copy the appearance of a signature, it is difficult to mimic the process and behavior of signing. Signature data can be captured via pens that incorporate sensors or through touch-sensitive surfaces which sense the unique signature characteristics. Touch-sensitive surfaces are increasingly being used on ICT devices such as screens, pads, mobile phones, laptops and tablet PCs.
Advantages:-
Main uses of signature biometrics include limiting access to restricted documents and contracts, delivery acknowledgement and banking/finance related applications
Disadvantages
A person’s signature changes over time as well as under physical and emotional influences
Multimodal Biometrics System:-
Multimodal biometric systems are those that utilize more than one physiological or behavioral characteristic for enrollment, verification, or identification. A biometric system which relies only on a single biometric identifier in making a personal identifications often not able to meet the desired performance requirements. Identification based on multiple biometrics represents on emerging trend. A multimodal biometric system is introduced which integrates face recognition, fingerprint verification, and speaker verification in making a personal identification. This system takes advantage of the capabilities of each individual biometric. It can be used to overcome some of the limitations of a single biometrics.
System Accuracy and Comparison
System Accuracy:-
Accuracy or performance of biometric systems is measured with three factors:
1. False acceptance rate (FAR)
2. False rejection rate (FRR)
3. Equal Error Rate (EER)
1. False Acceptance Rate:-
False acceptance rate is also known as Type I error. It measures the percentage of impostors being incorrectly accepted as genuine user. Since almost all biometric systems aim to achieve correct identity authentication, this number should be as low as possible.
2. False Rejection Rate:-
False rejection rate is also known as Type II error, this measures the percentage of genuine users being incorrectly rejected. In order to minimize inconveniences (or embarrassment) to the genuine user, this number should also be low.
3. Equal Error Rate:-
FAR and FRR are inversely related and a consolidation of the FAR and FFR is the point at which accept and reject errors are equal. This is described as the equal error rate (EER), sometimes also known as the cross-over error rate (CER). Low EER scores generally indicate high levels of accuracy. This is illustrated in Figure 9. FAR and FFR can often be adjusted by changing system parameters (rejection thresholds) or better control of conditions under which systems are used (dust free, good lighting and so on).
APPLICATIONS
Eye Gaze System:-
The Eye gaze Edge uses the pupil-center/corneal-reflection method to determine where the user is looking on the screen. An infrared-sensitive video camera, mounted beneath the System's screen, takes 60 pictures per second of the user's eye. A low power, infrared light emitting diode (LED), mounted in the center of the camera's lens illuminates the eye. The LED reflects a small bit of light off the surface of the eye's cornea. The light also shines through the pupil and reflects off of the retina, the back surface of the eye, and causes the pupil to appear white. The bright-pupil effect enhances the camera’s image of the pupil so the system's image processing functions can locate the center of the pupil. The Edge calculates the person's gaze point, i.e., the coordinates of where he is looking on the screen, based on the relative positions of the pupil center and corneal reflection within the video image of the eye. Typically the Eye gaze Edge predicts the gaze point with an average accuracy of a quarter inch or better. Prior to operating the eye tracking applications, the Eye gaze Edge must learn several physiological properties of a user's eye in order to be able to project his gaze point accurately. The system learns these properties by performing a calibration procedure. The user calibrates the system by fixing his gaze on a small circle displayed on the screen, and following it as it moves around the screen. The calibration procedure usually takes about 15 seconds, and the user does not need to recalibrate if he moves away from the Eye gaze Edge and returns later. A user operates the Eye gaze System by looking at rectangular keys that are displayed on the control screen. To "press" an Eye gaze key, the user looks at the key for a specified period of time. The gaze duration required to visually activate a key, typically a fraction of a second, is adjustable. An array of menu keys and exit keys allow the user to navigate around the Eye gaze programs independently.5
Television Controlled by Hand Gestures:- Hitachi launched a high-end TV panel working with the Canesta 3D sensor, which allows viewers interact with the TV controls via hand gestures. While the TVdisplays3Dimages we can wave our hand to power up the TV or move our hand circularly to change the channel. Canesta’s 3D sensor is immune to lighting extremes and works in any environment, whether it is indoors or outdoors, with the condition that we have to be within the 3-meter working range. It also distinguished between one hand and two hands and offers multiple commands depending on your hand’s motion. As we move our hands, the 3D sensor developed with CMOS chip technology sends a stream of 3D data at 30frames per second to the TVs micro-controller, where the gesture-recognition software translates the depth maps into gestures and then into commands.
Mimi Switch:- Mimi switch uses infrared sensors to measure movements inside the ear, which are triggered by various facial expressions, and then transmits signals to a micro-computer that controls electronic devices. It’s pretty much a hands-free remote control for anything electronic. It stores and can even interpret data, allowing it to customize itself to individual users, if it judges that we aren’t smiling enough, it may play a cheerful song.” In addition to its usefulness in controlling music devices or cell phones, it can also be used as safety measure, providing hearing aids for the elderly, or health monitors: It could measure,say, how often someone sneezes, and if it senses a serious health problem, it could send a warning message to relatives
Controller Free Gaming:- Project Natal is the code name for a "controller-free gaming and entertainment experience" by Microsoft for the Xbox 360video game platform. Project Natal enables users to control and interact with the Xbox 360 without the need to touch a game controller through a natural user interface using gestures, spoken commands or presented objects and images. The depth sensor consists of an infrared projector combined with a monochrome CMOS sensor , and allows the Project Natal sensor to see in 3D under any ambient light conditions. The sensing range of the depth sensor is adjustable, with the Project Natal software capable of automatically calibrating the sensor based on game play and the player's physical environment, such as the presence of chairs. Project Natal is likely based on software technology developed internally by Microsoft and 3D camera technology by Israeli developer Prime Sense, which interprets3D scene information from a continuous infrared pattern. It was initially reported that the hardware was acquired from time-of-flight camera developer 3DV Systems. Project Natal enables advanced gesture recognition, facial recognition, and voice recognition. The skeletal mapping technology was capable of simultaneously tracking up to four users for motion analysis with a feature extraction of 48skeletalpoints on a human body at frame rate of 30hertz. Depending on the person's distance from the sensor, Project Natal is capable of tracking models that can identify individual fingers.
Biometrics is basically used in door lock systems and can be used to prevent unauthorized access to ATMs, cellular phones, desktop PCs. It has largely used in access control and identity verifications, including time and attendance
Conclusion and Future Works
Conclusion:-
Biometric is an emerging area with many opportunities for growth. Biometrics is widely being used because of its user friendliness, flexibility in specifying required security level and long term stability. The technology will continue to improve and challenges such as interoperability solved through standardization. This will lead to increase in the market adoption rate and the technology will proliferate. Possibly in the near future, you will not have to remember PINs and passwords and keys in your bags or pockets will be things of the past.
Future works:-
The future of biometrics holds great promise for law enforcement applications, as well for private industry uses. Biometrics’ future will include e-commerce applications for extra security on the checkout page, and biometrics will guard against unauthorized access to cars and cell phones. In the future, biometric technology will further develop 3-D infrared facial recognition access control, real-time facial recognition passive surveillance, and visitor management authentication systems. Already A4Vision, a provider of 3-D facial scanning and identification software uses specialized algorithms to interpret the traditional 2-D camera image and transfer it into a 3-D representation of a registered face. This makes it almost impossible to deceive the biometric system with still photos or other images. Strengthening existing biometric innovations for future growth all of these security innovations will make biometric technology more accurate and make its usage more widespread.
Reference
Bromba, Dr. Manfred. "Biometrics FAQ." http://www.bromba.com/faq/biofaqe.htm#Biometrie
Brown, Greg. "Connecting the Dots." Latin Trade, April 2005. http://www.findarticles.com/p/articles/mi_m0BEK/is_4_13/ai_n13798070
FindBiometrics http://www.findbiometrics.com/
Homeland Security: Biometrics. GlobalSecurity.org http://www.globalsecurity.org/security/systems/biometrics.htm
Mainguet, Jean-François. "Biometrics." 2004. http://perso.wanadoo.fr/fingerchip/biometrics/biometrics.htm
Ross, A., S. Prabhakar and A. Jain. "An Overview of Biometrics." http://biometrics.cse.msu.edu/info.html
Resources Related to Biometrics and People with Disabilities http://www.icdri.org/biometrics/biometrics.htm
Thalheim, L., J. Krissler and P. Ziegler. "Body Check." WIBU Systems, November 2002. http://www.heise.de/ct/english/02/11/114/
The satisfaction and euphoria that accompany the successful completion of any task would be incomplete without the people who made it possible, whose constant guidance and encouragement crowned our efforts with success.
My primary thanks goes to Almighty Allah by whose Blessing I have completed my report in the given time. I would like to thank Prof. Zain Ul Abideen Valika for his patience, guidance and encouragement in successful completion of this report .
Finally, I would like to immensely thank my parents and all my friends for their constant support and encouragement which was the fuel propelling my effort.
Abstract
In this Hi-Tech era, there is a great demand to identify and authenticate the individuals. Till now we are totally dependent upon Passwords and Pin Numbers for identification. How secure are passwords? With the numerous passwords that an individual has to remember, they are often forgotten, …show more content…
misplaced, or stolen. Think of how many different passwords you have to remember: computer passwords, internet site logons and passwords, PIN numbers for the ATM and for credit cards, the list goes on. We are arriving at a conclusion that these technologies are not sufficient for the security of an individual as these are hard to remember, easily transferable, easily stolen and there are many weaknesses. Due to these weaknesses biometrics came into existence.
Biometrics is that study of science that deals with personal human behavioral and physiological characteristics and such as fingerprints, handprints, iris scanning, voice scanning, face recognition and signature recognition. These technologies are far more promising than that which are used currently to identify an individual. This paper highlights some of the benefits and the few limitations of using biometrics for authentication .With biometrics it doesn’t matter if we forget your password or lose your smartcard.
Contents
Introduction
The term "biometrics" is derived from the Greek words bio means “life” and metric means “to measure”. BIOMETRICS refers to the automatic identification of a person based on his physiological / behavioral characteristics. This method of identification is preferred for various reasons; the person to be identified is required to be physically present at the point of identification; identification based on biometric techniques obviates the need to remember a password or carry a token.
A biometric is a unique, measurable characteristic or trait for automatically recognizing or verifying the identity of a human being. Biometrics is a powerful combination of science and technology that can be used to protect and secure our most valuable information and property.
Recognition requires the system to look through many stored sets of characteristics and pick the one that matches the unknown individual being presented. Various types of biometric systems are being used for real–time identification; the most popular are based on face recognition and fingerprint matching. However there are other biometric systems that utilize iris and retinal scan, speech, gesture recognition , and hand geometry. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. The basic idea behind biometrics is that our bodies contain unique properties that can be used to distinguish us from others. A biometric system is essentially a pattern recognition system, which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristics possessed by the user. An important issue in designing a practical system is to determine how an individual is identified. Depending on the context, a biometric system can be either a verification (authentication) system an identification system. Verification involves confirming or denying a person’s claimed identity. In identification one has to establish a person’s identity. Identification systems based on biometrics are important building blocks in simplifying our interaction with the myriad digital systems and devices that we are all using in increasing numbers. There are levels of security from the most basic to the most robust with biometrics being the most secure:
Something that you have- such as an ID badge with a photograph on it.
Something that you know- such as a password or PIN number.
Something which you are- such as biometric data – fingerprints, iris, voice or face scans.
Biometrics is rapidly evolving technology, which is being used in forensics such as criminal identification and prison security, and has the potential to be used in a large range of civilian application areas. Biometrics can be used transactions conducted via telephone and Internet (electronic commerce and electronic banking). In automobiles, biometrics can replace keys with key -less entry devices.
How Biometric System Works
Biometric devices consist of a reader or scanning device, software that converts the gathered information into digital form, and a database that stores the biometric data for comparison with previous records.
When converting the biometric input, the software identifies specific points of data as match points. The match points are processed using an algorithm into a value that can be compared with biometric data in the database. The biometric feature must have the following characteristics:-(a) Universality, which means that every person should have the characteristic,(b) Uniqueness, two persons should not have the same term or measurement of Characteristic.(c) Permanence, the characteristic should be invariant with time.(d) Measurability, the characteristic can be quantified that is the origin of the Cameras used in biometric systems are generally either CCD (charge couple device) or CMOS(combined metal oxide semiconductor) image sensors. CCD is comparatively more costly than
CMOS.
The main operations a system can perform are enrollment and test. During the enrollment, biometric information from an individual is stored. During the test, biometric information is detected and compared with the stored information. Note that it is crucial that storage and retrieval of such systems themselves be secure if the biometric system is, robust.
The first block (sensor) is the interface between the real world and the system; it has to acquire all the necessary data. Most of the times it is an image acquisition system, but it can change according to the characteristics desired. A sample of the biometric trait is captured, processed by a computer, and stored for later comparison.
The second block performs all the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the input (e.g. removing background noise), to use some kind of normalization, etc.
In the third block features needed are extracted. This step is an important step as the correct features need to be extracted and the optimal way. A vector of numbers or an image with particular properties is used to create a template. A template is syntheses of all the characteristics extracted from the source, in the optimal size to allow for adequate identify ability. All Biometric authentications require comparing a registered or enrolled biometric sample (biometric template or identifier) against a newly captured biometric sample.
If enrollment is being performed where the biometric system identifies a person from the entire enrolled population by searching a database for a match based solely on the biometric. For example, an entire database can be searched to verify a person has not applied for entitlement benefits under two different names. This is sometimes called “one-to-many” matching.
If a verification phase is being performed, the biometric system authenticates a person’s claimed identity from their previously enrolled pattern. This is also called “one-to-one” matching. The obtained template is passed to a matcher that compares it with other existing templates. The matching program will analyze the template with the input. This will then be output for any specified use or purpose.
Classification of Biometrics
Biometrics encompasses both physiological and behavioral characteristics. A physiological characteristic are related to the shape of a body. A relatively stable physical feature such as finger print, hand geometry, iris pattern or facial features. These factors are basically unalterable without trauma to the individual. Behavioral tracts, on the other hand, are related to the behavior of a person. The most common trait used in identification is a person’s signature. Other behaviors used include a person’s keyboard typing, gait and speech patterns. Most of the behavioral characteristics change over time. Some of physical biometrics is
Fingerprint - analyzing fingertip patterns.
Facial Recognition- measuring facial characteristics
Hand Geometry- measuring the shape of the hand.
Iris recognition- analyzing features of colored ring of the eye.
Some of behavioral biometrics is
Speaker recognition- analyzing vocal behavior.
Signature recognition - analyze the physical activity of signing.
Gesture recognition - analyzing the motions of body
Humans have used fingerprints for personal identification for many centuries and the matching accuracy using fingerprints has been shown to be very high.
Finger printing
Finger printing is probably the best-known biometric- method of identification used for 100 years. There are a few variants of image capture technology available for such commercially oriented fingerprint sensor, including optical, silicon, ultrasound, thermal and hybrid. Among all the biometric techniques, fingerprint-based Identification is the oldest method that has been successfully used in numerous applications. Everyone is known to have unique, immutable fingerprints. A fingerprint is made of a series of ridges and furrows on the surface of the finger as shown in the fig 3.1.1. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as minutiae points. Minutiae points are the local ridge characteristics that occur either at a ridge ending or a ridge bifurcation. A ridge ending is defined as the point where the ridge ends abruptly and the ridge bifurcation is the point where the ridge splits into two or more branches. When a user places their finger on the terminals scanner the image is electronically read, analyzed, and compared with a previously recorded image of the same finger which has been stored in the database. The imaging process is based on digital holography, using an electro-optical scanner about the size of a thumbprint. The scanner reads three-dimensional data from the finger such as skin undulations, and ridges and valleys, to create a unique pattern that is composed into a template file.
An algorithm is developed to classify fingerprints into five classes, namely, whorl, right loop, arch and tented arch as shown in figure 3. Critical points in a finger print, called core and delta are marked on one of the fingers as shown in figure 3 (c). The cores the inner point, normally in the middle of the print, around which swirls, loops, or arches center. It is frequently characterized by a ridge ending and several acutely curved ridges. Deltas are the points, normally at the lower left and right hand of the fingerprint, around which a triangular series of ridges center. The algorithm separates the number of ridges present in four directions (o degree, 45 degree, 90 degree and 135 degree) by filtering the central part of a fingerprint with a bank of Gabor filters. This information is quantized to generate a finger code which is used for classification. To avoid fake-finger attacks, some systems employ so-called live detection technology, which takes advantage of the sweat activity of human bodies. High-magnification lenses and special illumination technologies capture the finger’s perspiration and pronounce the finger dead or alive. Advantages:-
Fingerprint recognition equipment is relatively low-priced compared to other biometric system.
Fingerprints are unique to each finger of each individual and the ridge arrangement remains permanent during one's lifetime.
Disadvantages:-
Some people have damaged or eliminated fingerprints.
Vulnerable to noise and distortion brought on by dirt and twists.
Handwriting
At first glance, using handwriting to identify people might not seem like a good idea. After all, many people can learn to copy other people's handwriting with a little time and practice. It seems like it would be easy to get a copy of someone's signature or the required password and learn to forge it.
But biometric systems don't just look at how you shape each letter; they analyze the act of writing. They examine the pressure you use and the speed and rhythm with which you write. They also record the sequence in which you form letters, like whether you add dots and crosses as you go or after you finish the word.
Unlike the simple shapes of the letters, these traits are very difficult to forge. Even if someone else got a copy of your signature and traced it, the system probably wouldn't accept their forgery.
A handwriting recognition system's sensors can include a touch-sensitive writing surface or a pen that contains sensors that detect angle, pressure and direction. The software translates the handwriting into a graph and recognizes the small changes in a person's handwriting from day to day and over time.
Hand and Finger Geometry
People's hands and fingers are unique -- but not as unique as other traits, like fingerprints or irises. That's why businesses and schools, rather than high-security facilities, typically use hand and finger geometry readers to authenticate users, not to identify them. Disney theme parks, for example, use finger geometry readers to grant ticket holders admittance to different parts of the park. Some businesses use hand geometry readers in place of timecards.
Systems that measure hand and finger geometry use a digital camera and light. To use one, you simply place your hand on a flat surface, aligning your fingers against several pegs to ensure an accurate reading. Then, a camera takes one or more pictures of your hand and the shadow it casts. It uses this information to determine the length, width, thickness and curvature of your hand or fingers. It translates that information into a numerical template.
Hand and finger geometry systems have a few strengths and weaknesses. Since hands and fingers are less distinctive than fingerprints or irises, some people are less likely to feel that the system invades their privacy. However, many people's hands change over time due to injury, changes in weight or arthritis. Some systems update the data to reflect minor changes from day to day.
For higher-security applications, biometric systems use more unique characteristics, like voices. We'll look at those next.
Advantages:-
Easy to capture.
The major advantage is that most people can use it and as such, the acceptance rate is good. Believed to be a highly stable pattern over the adult lifespan.
Disadvantages:-
Use requires some training. System requires a large amount of physical space.
Face recognition:- Face recognition technology analyze the unique shape, pattern and positioning of the facial features. Face recognition is very complex technology and is largely software based. Face recognition starts with a picture, attempting to find a person in the image. This can be accomplished using several methods including movement, skin tones, or blurred human shapes. The face recognition system locates the head and finally the eyes of the individual. A matrix is then developed based on the characteristics of the individual’s face. The method of defining the matrix varies according to the algorithm (the mathematical process used by the computer to perform the comparison). This matrix then compared to matrices that are in a database and a similarity score is generated for each comparison. Despite the fact that there are more reliable biometric recognition techniques such as fingerprint and iris recognition, these techniques are intrusive and their success depends highly on user cooperation, since the user must position her eye in front of their is scanner or put her finger in the fingerprint device. On the other hand, face recognition is non-intrusive since it is based on images recorded by a distant camera, and can be very effective even if the user is not aware of the existence of the face recognition system. The human face is undoubtedly the most common characteristic used by humans to recognize other people and this is why personal identification based on facial images is considered the friendliest among all biometrics. Face has certain distinguishable landmarks that are the peaks and valleys that sum up the different facial features. There are about 80 peaks and valleys on a human face. The following are a few of the peaks and valleys that are measured by the software
Distance between eyes
Width of nose
Depth of eye sockets
Cheekbones
Jaw line
Chin
These peaks and valleys are measured to give a numerical code, a string of numbers, which represents the face in a database. This code is called a face print. Face recognition involves the comparison of a given face with other faces in a database with the objective of deciding if the face matches any of the faces in that database.
1 1.Detection of the face in a complex background and localization of its exact position,
2 Extraction of facial features such as eyes, nose, etc., followed by normalization to align the face with the stored face images, and
3 Face classification or matching.
In addition, a face recognition system usually consists of the following four modules:
1 .Sensor module, which captures face images of an individual. Depending on the sensor modality, the acquisition device maybe a black and white or color camera, a 3D sensor capturing range (depth) data, or an infrared camera capturing infrared images.
2 Face detection and feature extraction module. The acquired face images are first scanned to detect the presence of faces and find their exact location and size. The output of face detection is an image window containing only the face area. Irrelevant information, such as background, hair, neck and shoulders, ears, etc. are discarded.
3 Classification module, in which the template extracted during step 2, is compared against the stored templates in the database to generate matching scores, which reveal how identical the faces in the probe and gallery images are. Then, a decision-making module either confirms (verification) or establishes (identification) the user’s identity based on the matching score. In case of face verification, the matching score is compared to a predefined threshold and based on the result of this comparison; the user is either accepted or rejected. In case of face identification, a set of matching scores between the extracted template and the templates of enrolled users is calculated. If the template of user X produces the best score, then the unknown face is more similar to X, than any other person in the database. To ensure that the unknown face is actually X and not an impostor, the matching score is compared to a predefined threshold.
4 Sometimes, more than one template per enrolled user is stored in the gallery database to account for different variations. Templates may also be updated over time, mainly to cope with variations due to aging.
Face detection algorithms can be divided into three categories according to
1 Knowledge-based methods are based on human knowledge of the typical human face geometry and facial features arrangement. Taking advantage of natural face symmetry and the natural top-to-bottom and left-to-right order in which features appear in the human face, these methods find rules to describe the shape, size, texture and other characteristics of facial features (such as eyes, nose, chin, eyebrows) and relationships between them (relative positions and distances). A hierarchical approach may be used, which examines the face at different resolution levels. At higher levels, possible face candidates are found using a rough description of face geometry. At lower levels, facial features are extracted and an image region is identified as face or non-face based on predefined rules about facial characteristics and their arrangement.
2 Feature invariant approaches aim to find structural features that exist even when the viewpoint or lighting conditions vary and then use these to locate faces. Different structural features are being used: facial local features, texture, and shape and skin color. Local features such as eyes, eyebrows, nose, and mouth are extracted using multi-resolution or derivative filters, edge detectors, morphological operations or thresholding. Statistical models are then built to describe their relationships and verify the existence of a face. Neural networks, graph matching, and decision trees were also proposed to verify face candidates.
3. Template-based methods. To detect a face in a new image, first the head outline, which is fairly consistently roughly elliptical, is detected using filters or edge detectors. Then the contours of local facial features are extracted in the same way, exploiting knowledge of face and feature geometry. More recently, techniques that rely on 3D shape data have been proposed. 3D face recognition is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. 3D face recognition has the potential to achieve better accuracy than its 2D counterpart by measuring geometry of rigid features on the face. This avoids such pitfalls of 2D face recognition algorithms as change in lighting, different facial expressions, make-up and head orientation.
Advantages:-
No contact required.
Commonly available sensors (cameras).
Disadvantages:-
Face can be obstructed by hair, glasses, hats, scarves etc.
Difficult to distinguish between twins.
Sensitive to changes in lighting, expression, and poses faces changeover time.
Iris Recognition:-
Iris scanning can seem very futuristic, but at the heart of the system is a simple CCD digital camera. It uses both visible and near-infrared light to take a clear, high-contrast picture of a person's iris. With near-infrared light, a person's pupil is very black, making it easy for the computer to isolate the pupil and iris.
When you look into an iris scanner, either the camera focuses automatically or you use a mirror or audible feedback from the system to make sure that you are positioned correctly. Usually, your eye is 3 to 10 inches from the camera. When the camera takes a picture, the computer locates:
The center of the pupil
The edge of the pupil
The edge of the iris
The eyelids and eyelashes
It then analyzes the patterns in the iris and translates them into a code.
An iris scanner
Iris scanners are becoming more common in high-security applications because people's eyes are so unique (the chance of mistaking one iris code for another is 1 in 10 to the 78th power [ref]. They also allow more than 200 points of reference for comparison, as opposed to 60 or 70 points in fingerprints.
The iris is a visible but protected structure, and it does not usually change over time, making it ideal for biometric identification. Most of the time, people's eyes also remain unchanged after eye surgery, and blind people can use iris scanners as long as their eyes have irises. Eyeglasses and contact lenses typically do not interfere or cause inaccurate readings.
Advantages:-
Iris recognition is very accurate with very low false acceptance rate
Disadvantages:-
Complex procedure.
High cost.
Speaker Recognition
Speaker, or voice, recognition is a biometric modality that uses an individual’s voice for recognition purposes. The speaker recognition process relies on features influenced by both the physical structure of an individual’s vocal tract and the behavioral characteristics of the individual. A popular choice for remote authentication due to the availability of devices for collecting speech samples and its ease of integration, speaker recognition is different from some other biometric methods in that speech samples are captured dynamically or over a period of time, such as a few seconds. Analysis occurs on a model in which changes over time are monitored.
Voice recognition technology utilizes the distinctive aspects of the voice to verify the identity of individuals. Voice recognition is occasionally confused with speech recognition, a technology which translates what a user is saying (a process unrelated to authentication). Voice recognition technology, by contrast, verifies the identity of the individual who is speaking. The two technologies are often bundled – speech recognition is used to translate the spoken word into an account number, and voice recognition verifies the vocal characteristics against those associated with this account.
Voice recognition can utilize any audio capture device, including mobile and land telephones and PC microphones. The performance of voice recognition systems can vary according to the quality of the audio signal as well as variation between enrollment and verification devices, so acquisition normally takes place on a device likely to be used for future verification. During enrollment an individual is prompted to select a passphrase or to repeat a sequence of numbers. Voice recognition can function as a reliable authentication mechanism for automated telephone systems, adding security to automated telephone-based transactions in areas such as financial services and health care. Certain voice recognition technologies are highly resistant to imposter attacks, means that voice recognition can be used to protect reasonably high-value transactions. samples are waveforms with time on the horizontal axis and loudness on the vertical access. The speaker recognition system analyzes the frequency content of the speech and compares characteristics such as the quality, duration, intensity dynamics, and pitch of the signal.
Voice recognition techniques can be divided into categories depending on the type of authentication domain.
Fixed text method is a technique where the speaker is required to say a predetermined word that is recorded during registration on the system.
In the text dependent method the system prompts the user to say a specific word or phrase, which is then computed on the basis of the user’s fundamental voice pattern.
The text independent method is an advanced technique where the user need not articulate any specific word or phrase. The matching is done by the system on the basis of the fundamental voice patterns irrespective of the language and the text used.
Advantages:-
Simple and cost-effective technological application.
Can be used for remote authentication.
Disadvantages:-
Voice and language usage change over time (e.g. as a result of age or illness).
Signature Recognition:-
Biometric signature recognition systems measure and analyze the physical activity of signing. Important characteristics include stroke order, the pressure applied, the pen-up movements, the angle the pen is held, the time taken to sign, the velocity and acceleration of the signature. Some systems additionally compare the visual image of signatures, though the focus in signature biometrics lies on writer-specific information rather than visual handwritten content. While it may appear trivial to copy the appearance of a signature, it is difficult to mimic the process and behavior of signing. Signature data can be captured via pens that incorporate sensors or through touch-sensitive surfaces which sense the unique signature characteristics. Touch-sensitive surfaces are increasingly being used on ICT devices such as screens, pads, mobile phones, laptops and tablet PCs.
Advantages:-
Main uses of signature biometrics include limiting access to restricted documents and contracts, delivery acknowledgement and banking/finance related applications
Disadvantages
A person’s signature changes over time as well as under physical and emotional influences
Multimodal Biometrics System:-
Multimodal biometric systems are those that utilize more than one physiological or behavioral characteristic for enrollment, verification, or identification. A biometric system which relies only on a single biometric identifier in making a personal identifications often not able to meet the desired performance requirements. Identification based on multiple biometrics represents on emerging trend. A multimodal biometric system is introduced which integrates face recognition, fingerprint verification, and speaker verification in making a personal identification. This system takes advantage of the capabilities of each individual biometric. It can be used to overcome some of the limitations of a single biometrics.
System Accuracy and Comparison
System Accuracy:-
Accuracy or performance of biometric systems is measured with three factors:
1. False acceptance rate (FAR)
2. False rejection rate (FRR)
3. Equal Error Rate (EER)
1. False Acceptance Rate:-
False acceptance rate is also known as Type I error. It measures the percentage of impostors being incorrectly accepted as genuine user. Since almost all biometric systems aim to achieve correct identity authentication, this number should be as low as possible.
2. False Rejection Rate:-
False rejection rate is also known as Type II error, this measures the percentage of genuine users being incorrectly rejected. In order to minimize inconveniences (or embarrassment) to the genuine user, this number should also be low.
3. Equal Error Rate:-
FAR and FRR are inversely related and a consolidation of the FAR and FFR is the point at which accept and reject errors are equal. This is described as the equal error rate (EER), sometimes also known as the cross-over error rate (CER). Low EER scores generally indicate high levels of accuracy. This is illustrated in Figure 9. FAR and FFR can often be adjusted by changing system parameters (rejection thresholds) or better control of conditions under which systems are used (dust free, good lighting and so on).
APPLICATIONS
Eye Gaze System:-
The Eye gaze Edge uses the pupil-center/corneal-reflection method to determine where the user is looking on the screen. An infrared-sensitive video camera, mounted beneath the System's screen, takes 60 pictures per second of the user's eye. A low power, infrared light emitting diode (LED), mounted in the center of the camera's lens illuminates the eye. The LED reflects a small bit of light off the surface of the eye's cornea. The light also shines through the pupil and reflects off of the retina, the back surface of the eye, and causes the pupil to appear white. The bright-pupil effect enhances the camera’s image of the pupil so the system's image processing functions can locate the center of the pupil. The Edge calculates the person's gaze point, i.e., the coordinates of where he is looking on the screen, based on the relative positions of the pupil center and corneal reflection within the video image of the eye. Typically the Eye gaze Edge predicts the gaze point with an average accuracy of a quarter inch or better. Prior to operating the eye tracking applications, the Eye gaze Edge must learn several physiological properties of a user's eye in order to be able to project his gaze point accurately. The system learns these properties by performing a calibration procedure. The user calibrates the system by fixing his gaze on a small circle displayed on the screen, and following it as it moves around the screen. The calibration procedure usually takes about 15 seconds, and the user does not need to recalibrate if he moves away from the Eye gaze Edge and returns later. A user operates the Eye gaze System by looking at rectangular keys that are displayed on the control screen. To "press" an Eye gaze key, the user looks at the key for a specified period of time. The gaze duration required to visually activate a key, typically a fraction of a second, is adjustable. An array of menu keys and exit keys allow the user to navigate around the Eye gaze programs independently.5
Television Controlled by Hand Gestures:- Hitachi launched a high-end TV panel working with the Canesta 3D sensor, which allows viewers interact with the TV controls via hand gestures. While the TVdisplays3Dimages we can wave our hand to power up the TV or move our hand circularly to change the channel. Canesta’s 3D sensor is immune to lighting extremes and works in any environment, whether it is indoors or outdoors, with the condition that we have to be within the 3-meter working range. It also distinguished between one hand and two hands and offers multiple commands depending on your hand’s motion. As we move our hands, the 3D sensor developed with CMOS chip technology sends a stream of 3D data at 30frames per second to the TVs micro-controller, where the gesture-recognition software translates the depth maps into gestures and then into commands.
Mimi Switch:- Mimi switch uses infrared sensors to measure movements inside the ear, which are triggered by various facial expressions, and then transmits signals to a micro-computer that controls electronic devices. It’s pretty much a hands-free remote control for anything electronic. It stores and can even interpret data, allowing it to customize itself to individual users, if it judges that we aren’t smiling enough, it may play a cheerful song.” In addition to its usefulness in controlling music devices or cell phones, it can also be used as safety measure, providing hearing aids for the elderly, or health monitors: It could measure,say, how often someone sneezes, and if it senses a serious health problem, it could send a warning message to relatives
Controller Free Gaming:- Project Natal is the code name for a "controller-free gaming and entertainment experience" by Microsoft for the Xbox 360video game platform. Project Natal enables users to control and interact with the Xbox 360 without the need to touch a game controller through a natural user interface using gestures, spoken commands or presented objects and images. The depth sensor consists of an infrared projector combined with a monochrome CMOS sensor , and allows the Project Natal sensor to see in 3D under any ambient light conditions. The sensing range of the depth sensor is adjustable, with the Project Natal software capable of automatically calibrating the sensor based on game play and the player's physical environment, such as the presence of chairs. Project Natal is likely based on software technology developed internally by Microsoft and 3D camera technology by Israeli developer Prime Sense, which interprets3D scene information from a continuous infrared pattern. It was initially reported that the hardware was acquired from time-of-flight camera developer 3DV Systems. Project Natal enables advanced gesture recognition, facial recognition, and voice recognition. The skeletal mapping technology was capable of simultaneously tracking up to four users for motion analysis with a feature extraction of 48skeletalpoints on a human body at frame rate of 30hertz. Depending on the person's distance from the sensor, Project Natal is capable of tracking models that can identify individual fingers.
Biometrics is basically used in door lock systems and can be used to prevent unauthorized access to ATMs, cellular phones, desktop PCs. It has largely used in access control and identity verifications, including time and attendance
Conclusion and Future Works
Conclusion:-
Biometric is an emerging area with many opportunities for growth. Biometrics is widely being used because of its user friendliness, flexibility in specifying required security level and long term stability. The technology will continue to improve and challenges such as interoperability solved through standardization. This will lead to increase in the market adoption rate and the technology will proliferate. Possibly in the near future, you will not have to remember PINs and passwords and keys in your bags or pockets will be things of the past.
Future works:-
The future of biometrics holds great promise for law enforcement applications, as well for private industry uses. Biometrics’ future will include e-commerce applications for extra security on the checkout page, and biometrics will guard against unauthorized access to cars and cell phones. In the future, biometric technology will further develop 3-D infrared facial recognition access control, real-time facial recognition passive surveillance, and visitor management authentication systems. Already A4Vision, a provider of 3-D facial scanning and identification software uses specialized algorithms to interpret the traditional 2-D camera image and transfer it into a 3-D representation of a registered face. This makes it almost impossible to deceive the biometric system with still photos or other images. Strengthening existing biometric innovations for future growth all of these security innovations will make biometric technology more accurate and make its usage more widespread.
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