Human Detection and Identification by Robots

J Intell Robot Syst (2012) 66:223–243
DOI 10.1007/s10846-011-9612-2

Human Detection and Identification by Robots
Using Thermal and Visual Information in Domestic Environments
Mauricio Correa · Gabriel Hermosilla ·
Rodrigo Verschae · Javier Ruiz-del-Solar

Received: 11 December 2010 / Accepted: 30 May 2011 / Published online: 12 July 2011
© Springer Science+Business Media B.V. 2011

Abstract In this paper a robust system for enabling robots to detect and identify humans in domestic environments is proposed. Robust human detection is achieved through the use of thermal and visual information sources that are integrated to detect human-candidate objects, which are further processed in order to verify the presence of humans and their identity using face information in the thermal and visual spectrums. Face detection is used to verify the presence of humans, and face recognition to identify them. Active vision mechanisms are employed in order to improve the relative pose of a candidate object/person in case

M. Correa · G. Hermosilla · R. Verschae ·
J. Ruiz-del-Solar
Department of Electrical Engineering,
Universidad de Chile, Av. Tupper 2007,
Santiago, Chile
M. Correa e-mail: [e-mail]
G. Hermosilla e-mail: [e-mail]
J. Ruiz-del-Solar e-mail: [e-mail]
M. Correa · G. Hermosilla · R. Verschae (B) ·
J. Ruiz-del-Solar
Advanced Mining Technology Center,
Universidad de Chile, Av. Tupper 2007,
Santiago, Chile e-mail: [e-mail]

direct identification is not possible. The response of the different modules is characterized, and the proposed system is validated using image databases of real domestic environments, and human detection and identification benchmarks of the
RoboCup@Home research community.
Keywords Human detection · Human identification · Service robot · Thermal image

1 Introduction
There is increasing interest in domestic service robots in the robotics community. A domestic service robot is a subclass of mobile

References: 94(11), 1948–1962 (2006) 2 Sci. (RoboCup Symposium 2010) (2010, in press) 3 Signal Processing Perspective), vol. 2009, Article ID 184617, p review. J. Comput. Vis. Image Understanding 97(1), 103–135 (2005) sensing. 2004 IEEE Intelligent Vehicles Symposium, pp 8. Mudaly, S.S.: Novel computer-based infrared pedestrian data-acquisition system. Electron. Lett. 15(13), 371–372 (1979) Veh. Symposium 1, 15–20 (2002) 242 Trans. Veh. Technol. 53(6), 1666–1678 (2004) 11 Sci. 3546, 527–531 (2005) 12 IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 627–639 (2007) (2005) 15 Mach. Vis. Appl. 19(2), 85–103 (2008) 16 Symposium 2008) 5399, 473–484 (2009) 17 Surv. 35(4), 399–458 (2003) 18 Recogn. 39, 1725–1745 (2006) 19 machine recognition of faces: a survey. Proc. IEEE 83(5), 705–740 (1995) Cognitive Neurosicence 3(1), 71–86 (1991) 22 2004), 24–27 October 2004, pp. 207–210. IEEE Press, Singapore (2004) 25. Jones, M., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. Comput. Vis. 46(1), 81–96 (2002) Comput. Vis. Image Understanding, 83(3), 236–274 (2001) 27. Satake, J., Miura, J.: Robust Stereo-based Person Detecting and Tracking for a Person Following Robot. In: J Intell Robot Syst (2012) 66:223–243