[en] Current trends seem to accredit gait as a sensible biometric feature for human identification, at least in a
multimodal system. In addition to being a robust feature, gait is hard to fake and requires no cooperation
from the user. As in many video systems, the recognition confidence relies on the angle of view of the
camera and on the illumination conditions, inducing a sensitivity to operational conditions that one
may wish to lower.
In this paper we present an efficient approach capable of recognizing people in frontal-view video
sequences. The approach uses an intra-frame description of silhouettes which consists of a set of rectangles
that will fit into any closed silhouette. A dynamic, inter-frame, dimension is then added by aggregating
the size distributions of these rectangles over multiple successive frames. For each new frame, the
inter-frame gait signature is updated and used to estimate the identity of the person detected in the
scene. Finally, in order to smooth the decision on the identity, a majority vote is applied to previous
results. In the final part of this article, we provide experimental results and discuss the accuracy of the
classification for our own database of 21 known persons, and for a public database of 25 persons.
Centre/Unité de recherche :
Intelsig Telim
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
Barnich, Olivier ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Techniques du son et de l'image
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Langue du document :
Anglais
Titre :
Frontal-view gait recognition by intra- and inter-frame rectangle size distribution
Barnich O., Jodogne S., and Van Droogenbroeck M. Robust analysis of silhouettes by morphological size distributions. Lecture Notes on Computer Science vol. 4179 (2006), Springer-Verlag pp. 734-745
BenAbdelkader, C., Cutler, R., Davis, L., 2002. Motion-based recognition of people in EigenGait space. In: Proc. 5th IEEE Internat. Conf. on Automatic Face and Gesture Recognition, pp. 267-272.
Boulgouris N.V., Hatzinakos D., and Plataniotis K.N. Gait recognition: A challenging signal processing technology for biometric identification. IEEE Signal Process. Mag. 22 6 (2005) 78-90
Boulgouris N.V., Plataniotis K.N., and Hatzinakos D. Gait recognition using linear time normalization. Pattern Recognition 39 5 (2006) 969-979
Breiman L. Bagging predictors. Mach. Learn. 26 2 (1996) 123-140
Breiman L. Random forests. Mach. Learn. 45 1 (2001) 5-32
Cunado, D., Nixon, M., Carter, J., 1999. Automatic gait recognition via model-based evidence gathering. In: Proc. IEEE Workshop on Automated ID Technologies (AutoID99), pp. 27-30.
Foster J., Nixon M., and Prügel-Bennett A. Automatic gait recognition using area-based metrics. Pattern Recognition Lett. 24 14 (2003) 2489-2497
Geurts P., Ernst D., and Wehenkel L. Extremely randomized trees. Mach. Learn. 36 1 (2006) 3-42
Grimson W. Gait analysis for recognition and classification. Proc. 5th IEEE Internat. Conf. on Automatic Face and Gesture Recognition (FGR 02) (2002), IEEE Computer Society, Washington, DC, USA 155-161
Gross, R., Shi, J., 2001. The CMU motion of body (MoBo) database. Technical Report CMU-RI-TR-01-18, Robotics Institute, Pittsburgh, PA.
Hu W., Tan T., Wang L., and Maybank S. A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybernet. 34 3 (2004) 334-352
Huang X., and Boulgouris V. Human gait recognition based on multiview gait sequences. EURASIP J. Adv. Signal Process. 8 (2008) 2008
Kale, A., Cuntoor, N., Yegnanarayana, B., Rajagopalan, A., Chellappa, R., 2003. Gait analysis for human identification. In: Proc. Internat. Conf. on Audio- and Video-Based Person Authentication, Guildford, UK, pp. 706-714.
Kale A., Sundaresan A., Rajagopalan A., Cuntoor N., Roy-Chowdhury A., Kruger V., and Chellappa R. Identification of humans using gait. IEEE Trans. Image Process. 13 9 (2004) 1163-1173
Lee S., Liu Y., and Collins R. Shape variation-based frieze pattern for robust gait recognition. IEEE Internat. Conf. Comput. Vision Pattern Recognition (2007) 1-8
Liu Y., Collins R., and Tsin Y. Gait sequence analysis using frieze patterns. Proc. 7th European Conf. Computer Vision-Part II (2002), Springer-Verlag, London, UK 657-671
Liu Z., and Sarkar S. Outdoor recognition at a distance by fusing gait and face. Image Vision Comput. 25 6 (2007) 817-832
Mowbray S., and Nixon M. Automatic gait recognition via Fourier descriptors of deformable objects. In: Kittler J., and Nixon M. (Eds). Audio Visual Biometric Person Authentication (2003), Springer 566-573
Nixon M., Carter J., Shutler J., and Grant M. New advances in automatic gait recognition. Elsevier Information Security Technical Report 7 4 (2002) 23-35
Nixon M., Tan T., and Chellappa R. Human Identification Based on Gait (2006), Springer
Niyogi, S., Adelson, E., 1994. Analyzing and recognizing walking figures in XYT. In: Proc. IEEE Internat. Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 469-474.
Serra J. Image Analysis and Mathematical Morphology (1982), Academic Press, New York
Soriano M., Araullo A., and Saloma C. Curve spreads: A biometric from front-view gait video. Pattern Recognition Lett. 25 14 (2004) 1595-1602
Stauffer C., and Grimson E. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22 8 (2000) 747-757
Wang L., Tan T., Ning H., and Hu W. Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25 12 (2003) 1505-1518
Yam C., Nixon M., and Carter J. Automated person recognition by walking and running via model-based approaches. Pattern Recognition Lett. 37 5 (2004) 1057-1072
Zhou S., Chellappa R., and Zhao W. Unconstrained Face Recognition (2006), Springer
Zhou X., and Bhanu B. Feature fusion of side face and gait for video-based human identification. Pattern Recognition 41 3 (2008) 778-795
Zivkovic, Z., 2004. Improved adaptive Gaussian mixture model for background subtraction. In: Proc. 17th Internat. Conf. on Pattern Recognition, vol. 2, pp. 28-31.