Abstract
Gait identification task becomes more difficult due to the change of appearance by different cofactors (e.g., shoe, surface, carrying, view, and clothing). The cofactors may affect some parts of gait while other parts remain unchanged and can be used for recognition. We propose a robust technique to define which parts are more effective and which parts are less effective for cofactors like clothing, carrying objects etc. To find out the effective body parts, the whole body is divided into small segments where each segment is a single row in this paper. Based on positive and negative effect of each segment, three most effective parts and two less effective parts are defined. Usually, the dynamic areas (e.g., legs, arms swing) are comparatively less affected than static areas (e.g., torso) for different cofactors in appearance based gait representation. To give more emphasis on dynamic areas and less on static areas, frequency-domain gait entropy termed as EnDFT representation is computed and used as gait features. Experiments are conducted on two comprehensive benchmarking databases: The OU-ISIR Gait Database, the Treadmill dataset B with clothing variations and CASIA Gait Database, Dataset B with clothing and carrying conditions. The proposed method shows better results in comparison with other existing gait recognition approaches.
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Notes
We mean a method of directly matching the whole human body without any part selection by a term “whole-based methods.
References
Ariyanto G, Nixon M (2012) Marionette mass-spring model for 3d gait biometrics. In: Proceedings of the 5th IAPR international conference on biometrics. New Delhi, pp 354–359
Bashir K, Xiang T, Gong S (2009) Gait recognition using gait entropy image. In: Proceedings of 3rd international conference on crime detection and prevention. London, pp 1–6
Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recogn Lett 31:2052–2060
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720
BenAbdelkader C, Culter R, Nanda H, Davis L (2001) Eigengait: motion-based recognition people using image self-similarity. In: Proceedings of the international conference on audio and video-based person authentication. Halmstad, pp 284–294
Boulgouris N, Chi Z (2007) Human gait recognition based on matching of body components. Pattern Recogn 40:1763–1770
Boulgouris NV, Hatzinakos D, Plataniotis KN (2005) Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Proc Mag 22:78–90
Cuntoor N, Kale A, Chellappa R (2003) Combining multiple evidences for gait recognition. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing. Hong Kong, pp 33–36
Dawson M (2002) Gait recognition final report. Department of Computing, Imperial College of Science, Technology and Medicine, London
Dempster W, Gaughran G (1967) Properties of body segments based on size and weight. Am J Anat 120:33–54
Guan Y, Li C-T, Hu Y (2012) Robust clothing-invariant gait recognition. In: Proceedings of eighth international conference on intelligent information hiding and multimedia signal processing. Piraeus-Athens, pp 321–3324
Han J, Bhanu B (2006) Individual recognition using gait energy image. Trans Pattern Anal Mach Intell 28:316–322
Hossain M, Makihara Y, Wang J, Yagi Y (2010) Clothing invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recogn 43:2281–2291
Jang-Hee Y, Doosung H, Ki-Young M, Nixon MS (2008) Automated human recognition by gait using neural network. In: Proceedings of first workshops on image processing theory, tools and applications. Sousse, pp 1–6
Kuncheva LI, Roli F, Marcialis G, Shipp C (2001) Complexity of data subsets generated by random subspace method: an experimental investigation, multiple classifier systems. In: Proceedings of 2nd international workshop on multiple classifier systems, vol 2096. pp 349–358
Lam T, Cheung K, Liu J (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recogn 44:973–987
Lee L, Grimson W (2002) Gait analysis for recognition and classification. In: Proceedings of the fifth IEEE conference on face and gesture recognition. Washington DC, pp 148–155
Li X, Maybank S, Yan S, Tao D, Xu D (2008) Gait components and their application to gender recognition. IEEE Trans Syst Man Cybern 38:145–155
Liu Y, Collins R, Tsin Y (2002) Gait sequence analysis using frieze patterns. In: Proceedings of the 7th European conference on computer vision. Copenhagen, pp 657–671
Liu Z, Sarkar S (2004) Simplest representation yet for gait recognition: averaged silhouette. In: Proceedings of international conference on pattern recognition (ICPR). Cambridge, pp 211–214
Lu H, Plataniotis K, Venetsanopoulos A (2007) Uncorrelated multilinear discriminant analysiswith regularization for gait recognition. In: Proceedings of biometrics symposium. Baltimore, pp 1–6
Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain. In: Proceedings of the ninth European conference on computer vision. Graz, pp 151–163
Makihara Y, Mannami H, Tsuji A, Hossain M, Sugiura K, Mori A, Yagi Y (2012) The ou-isir gait database comprising the treadmill dataset. IPSJ Trans Comput Vis Appl 4:53–62
Nixon M, Carter J (2006) Automatic recognition by gait. In: Proceedings of the IEEE, pp 2013–2024
Nixon M, Tan T, Chellappa R (2010) Human identification on gait. Springer, Berlin
Rogers E Gait recognition. In: Bell Canada chair in multimedia IPSI: Identity, Privacy and Security Initiative. Department of Electrical and Computer Engineering, University of Toronto
Rokanujjaman M, Hossain M, Islam M (2012) Effective part selection for part-based gait identification. In: Proceedings of the 7th international conference on electrical and computer engineering. BUET, pp 17–19
Sarkar S, Phillips P, Liu Z, Vega I, Grother P, Bowyer K (2005) The humanid gait challenge problem: data sets, performance, and analysis. Trans Pattern Anal Mach Intell 27:162–177
Urtasun R, Fua P (2004) 3d tracking for gait characterization and recognition. In: Proceedings of the 6th IEEE international conference on automatic face and gesture recognition. Seoul, pp 17–22
Wagg D, Nixon M (2004) On automated model-based extraction and analysis of gait. In: Proceedings of the 6th IEEE international conference on automatic face and gesture recognition. Seoul, pp 11–16
Wang J, She M, Nahavandi S, Kouzani A (2010) A review of vision-based gait recognition methods for human identification. In: Proceedings of international conference on digital image computing techniques and applications (DICTA). Sydney, pp 320–327
Wang L, Tan T, Ning H, Hu W (2003) Silhouette analysis based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25:1505–1518
Wang C, Zhang J, Wang L, Pu J, Yuan X (2012) Human identification using temporal information preserving gait template. IEEE Trans Pattern Anal Mach Intell 34:2164–2176
Yam C, Nixon M, Carter J (2004) Automated person recognition by walking and running via model-based approaches. Pattern Recogn 37:1057–1072
Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proceedings of 18th international conference on pattern recognition. Hong Kong, pp 441–444
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Rokanujjaman, M., Islam, M., Hossain, M. et al. Effective part-based gait identification using frequency-domain gait entropy features. Multimed Tools Appl 74, 3099–3120 (2015). https://doi.org/10.1007/s11042-013-1770-8
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DOI: https://doi.org/10.1007/s11042-013-1770-8