Face Recognition System
Chapter II :
Review of Related Literature
Related literatures refer to the list of reference related to the study being conducted. This will serve as a guide.
According to: Anil K. Jain, Brendan Klare, and Unsang Park of Michigan State University.
Face Recognition
Face recognition is the task of recognizing a person using digital face images. A FRS is typically designed to output a measure of similarity between two face images. Automated FRSs typically involve finding key facial landmarks (such as the center of the eyes) for alignment, normalizing the face’s appearance, choosing a suitable feature representation, learning discriminative feature combinations, and developing accurate and scalable matching schemes.
Two decades of vigorous research has yielded face-recognition systems that are highly accurate in constrained environments. However, the face-recognition community has recognized four key factors that significantly compromise recognition accuracy: pose, illumination, expression, and aging.
Face images in government-issued identification documents (such as driver’s licenses and passports) and mug shots are two scenarios that offer such constraints, which has led to success in the de-duplication (that is, matching process to detect ID cards enrolled under different names but belonging to the same subject) of identification cards and prevention of false prisoner releases.
Paradigm for Forensic Face Recognition
In forensic identification, investigators must use any available information to facilitate subject identification. Typically, the sources of face images are surveillance cameras, mobile device cameras, forensic sketches, and images from social media sites. These face images are difficult to match because they are often captured under non-ideal conditions. Non-forensic, fully automated scenarios are not severely impacted by these performance degrading factors. As a result, forensic face recognition often requires a preprocessing stage
Review of Related Literature
Related literatures refer to the list of reference related to the study being conducted. This will serve as a guide.
According to: Anil K. Jain, Brendan Klare, and Unsang Park of Michigan State University.
Face Recognition
Face recognition is the task of recognizing a person using digital face images. A FRS is typically designed to output a measure of similarity between two face images. Automated FRSs typically involve finding key facial landmarks (such as the center of the eyes) for alignment, normalizing the face’s appearance, choosing a suitable feature representation, learning discriminative feature combinations, and developing accurate and scalable matching schemes.
Two decades of vigorous research has yielded face-recognition systems that are highly accurate in constrained environments. However, the face-recognition community has recognized four key factors that significantly compromise recognition accuracy: pose, illumination, expression, and aging.
Face images in government-issued identification documents (such as driver’s licenses and passports) and mug shots are two scenarios that offer such constraints, which has led to success in the de-duplication (that is, matching process to detect ID cards enrolled under different names but belonging to the same subject) of identification cards and prevention of false prisoner releases.
Paradigm for Forensic Face Recognition
In forensic identification, investigators must use any available information to facilitate subject identification. Typically, the sources of face images are surveillance cameras, mobile device cameras, forensic sketches, and images from social media sites. These face images are difficult to match because they are often captured under non-ideal conditions. Non-forensic, fully automated scenarios are not severely impacted by these performance degrading factors. As a result, forensic face recognition often requires a preprocessing stage