Evaluation of human silhouette detection methods for a non-cooperative biometric system

Biometric authentication systems are becoming increasingly popular and widespread as the demand for automated people identification increases. Although, several research works focused their efforts on these type of solutions, none of the commonly available systems provide a non-cooperative approach to object identification. For this reason, they are not suitable for use in some specific situations, such as people entering the stadium. In this paper, we present an evaluation of different algorithms suitable for person detection in such environment. We focus on investigating their performance and effectiveness under unconstrained conditions, such as different lighting.

[1]  Andrzej Napieralski,et al.  Hardware Architecture Optimized for Iris Recognition , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Navneet Dalal,et al.  Finding People in Images and Videos , 2006 .

[3]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  William T. Freeman,et al.  Orientation Histograms for Hand Gesture Recognition , 1995 .

[6]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[7]  Z. Zivkovic Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[8]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[10]  F.W. Wheeler,et al.  Stand-off Iris Recognition System , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[11]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[12]  Wojciech Sankowski,et al.  Human Tracking in Non-cooperative Scenarios , 2014 .

[13]  Sergiu Nedevschi,et al.  A comparative study of pedestrian detection methods using classical Haar and HoG features versus bag of words model computed from Haar and HoG features , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[14]  Bernt Schiele,et al.  Visual People Detection - Different Models, Comparison and Discussion , 2009, ICRA 2009.