Abstract
In visual surveillance applications, gait is the preferred candidate for recognition of the identity of the subject under consideration. Gait is a behavioral biometric that has a large amount of redundancy, complex pattern distribution and very large variability, when multiple covariate exist. This demands robust representation and computationally efficient statistical processing approaches for improved performance. In this paper, a robust representation approach called Normal Distance Map and multilinear statistical discriminant analysis called Sparse Multilinear Discriminant Analysis is applied for improving robustness against covariate variation and increase recognition accuracy. Normal Distance Map captures geometry and shape of silhouettes so as to make representation robust and Sparse Multilinear Discriminant Analysis obtains projection matrices to preserve discrimination.
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Makihara Y, Matovski DS, Nixon MS, Carter JN, Yagi Y (2015) Gait recognition: databases, representations, and applications. Wiley Online Library
Sivarathinabala M, Abirami S, Baskaran R (2017) A study on security and surveillance system using gait recognition. In: Intelligent techniques in signal processing for multimedia security. Springer, Berlin, pp 227–252
Zhang Z, Hu M, Wang Y (2011) A survey of advances in biometric gait recognition. In: Chinese conference on biometric recognition. Springer, Berlin, pp 150–158
Boulgouris NV, Hatzinakos D, Plataniotis KN (2005) Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Process Mag 22(6):78–90
Wang J, She M, Nahavandi S, Kouzani A (2010) A review of vision-based gait recognition methods for human identification. Digit Image Comput: Tech Appl pp 320–327
Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28:316–322
Huang X, Boulgouris NV (2012) Gait recognition with shifted energy image and structural feature extraction. IEEE Trans Image Process 21:2256–2268
Bashir K, Xiang T, Gong S (2009) Gait recognition using gait entropy image. In: In 3rd international conference on crime detection and protection, London, UK
Lam THW, Cheung K, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recogn 44:973–987
Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain. In: European conference on computer vision. Springer, Berlin, pp 151–163
Hofmann M, Bachmann S, Rigoll G (2012) 2.5 d gait biometrics using the depth gradient histogram energy image. In: 2012 IEEE fifth international conference on biometrics: theory, applications and systems (BTAS). IEEE, New York, pp 399–403
El-Alfy H, Mitsugami I, Yagi Y (2014) A new gait-based identification method using local gauss maps. In: Asian conference on computer vision. Springer, Berlin, pp 3–18
Tang S, Wang X, Lv X, Han TX, Keller J, He Z, Skubic M, Lao S (2012) Histogram of oriented normal vectors for object recognition with a depth sensor. In: Asian conference on computer vision. Springer, Berlin, pp 525–538
El-Alfy H, Mitsugami I, Yagi Y (2017) Gait recognition based on normal distance maps. IEEE Trans Cybern
Gauss KF (1902) General investigations of curved surfaces of 1827 and 1825
Hazewinkel M (2001) Encyclopaedia of mathematics, vol 13. Springer, Berlin
Chhatrala R, Patil S, Lahudkar S, Jadhav DV (2017) Sparse multilinear Laplacian discriminant analysis for gait recognition. Pattern Anal Appl pp 1–14
Xu D, Huang Y, Zeng Z, Xu X (2012) Human gait recognition using patch distribution feature and locality-constrained group sparse representation. IEEE Trans Image Process 21(1):316–326
Sarkar S, Phillips P, Liu Z, Vega IR, Grother P, Bowyer K (2005) The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27:166–177
Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inf Forensics Secur 7(5):1511–1521
Wang C, Zhang J, Pu J, Yuan X, Wang L (2010) Chrono-gait image: a novel temporal template for gait recognition. In: European conference on computer vision. Springer, Berlin, pp 257–270
Guan Y, Li CT, Roli F (2015) On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 37(7):1521–1528
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Chhatrala, R., Patil, S., Jadhav, D.V. (2019). Gait Recognition Using Normal Distance Map and Sparse Multilinear Laplacian Discriminant Analysis. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_14
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DOI: https://doi.org/10.1007/978-3-030-00665-5_14
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