Abstract
Distinct and unique ways of walking have attracted the attention of researchers for identification of different individuals. The Gait; the walking video sequence offers unique advantages like the capability of capturing from a distance in a non-cooperative environment and providing recognition without being obtrusive. These advantages has demonstrated applications of gait recognition during intelligent video surveillance. The paper presents new discriminative feature extraction and dimensionality reduction scheme called as Sparse Multilinear Laplacian Discriminant Analysis (SMLDA) for gait recognition. SMLDA exploits the benefits of Laplacian weighted scatter difference instead of simple scatter difference and sparsity constraint as a class separability measure. The performance of the proposed scheme has been evaluated experimentally on CASIA, USF and OU-ISIR datasets. The experimental results show the competitive performance in comparison with conventional gait recognition schemes.



Similar content being viewed by others
References
Bashir K (2010) Robust gait recognition under variable covariate conditions. Ph.D. thesis, Queen Mary University of London, London
Bashir K, Xiang T, Gong S (2009) Gait recognition using gait entropy image. In: International conference on crime detection and prevention. London, UK
BenAbdelkader C (2002) Gait as a biometric for person identification in video. Ph.D. thesis, University of Maryland
Bodor R, Drenner A, Fehr D, Masoud O, Papanikolopoulos N (2009) View-independent human motion classification using image-based reconstruction. Image Vis Comput 27(8):1194–1206. doi:10.1016/j.imavis.2008.11.008
Boulgouris NV, Plataniotis KN, Hatzinakos D (2004) An angular transform of gait sequences for gait assisted recognition. In: 2004 International conference on image processing, ICIP’04, vol 2. IEEE, pp 857–860
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, pp 3–18
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
Lan TH, Cheung KH, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit 44:973–987
Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28:316–322
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, pp 399–403
Hu RX, Jia W, Huang DS, Lei YK (2010) Maximum margin criterion with tensor representation. Neurocomputing 73(10):1541–1549
Huang X, Boulgouris NV (2012) Gait recognition with shifted energy image and structural feature extraction. IEEE Trans Image Process 21:2256–2268
Huang Y, Xu D, Cham TJ (2010) Face and human gait recognition using image-to-class distance. IEEE Trans Circuits Syst Video Technol 20(3):431–438
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
Lai Z, Xu Y, Chen Q, Yang J, Zhang D (2014) Multilinear sparse principal component analysis. IEEE Trans Neural Netw Learn Syst 25(10):1942–1950
Lai Z, Xu Y, Jin Z, Zhang D (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662
Lai Z, Xu Y, Yang J, Tang J, Zhang D (2013) Sparse tensor discriminant analysis. IEEE Trans Image Process 22:3904–3915
Liu Y, Collins R, Lee S (2007) Shape variation-based frieze pattern for robust gait recognition. In: CVPR, pp 1–8
Liu Z, Sarkar S (2005) Effect of silhouette quality on hard problems in gait recognition. IEEE Trans Systems Man Cybern Part B Cybern 35(2):170–183
Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19:18–39
Lu H, Plataniotis KN, Venetsanopoulos AN (2009) Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition. IEEE Trans Neural Netw 20:103–123
Lu H, Plataniotis KN, Venetsanopoulos AN (2009) Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning. IEEE Trans Neural Netw 20:1820–1836
Lu H, Plataniotis KN, Venetsanopoulos AN (2011) A survey of multilinear subspace learning for tensor data. Pattern Recognit 44:1540–1551
Makihara Y, Matovski DS, Nixon MS, Carter JN, Yagi Y (2015) Gait recognition: databases, representations, and applications. Wiley Encycl Electr Electron Eng
Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain. In: Computer vision—ECCV 2006. Springer, pp 151–163
Mowbray SD, Nixon MS (2003) Automatic gait recognition via fourier descriptors of deformable objects. In: Audio and video based biometric person authentication, pp 566–573
Nie F, Xiang S, Song Y, Zhang C (2009) Extracting the optimal dimensionality for local tensor discriminant analysis. Pattern Recognit 42(1):105–114
Nixon M, Carter J (2006) Automatic recognition by gait. IEEE Trans Image Video Process 94:2013–2024
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
Sun J, Tao D, Papadimitriou S, Yu PS, Faloutsos C (2008) Incremental tensor analysis: theory and applications. ACM Trans Knowl Discov Data TKDD 2(3):11
Tao D, Li X, Wu X, Maybank S (2007) General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29:1700–1715
Tao D, Li X, Wu X, Maybank S (2008) Tensor rank one discriminant analysis a convergent method for discriminative multilinear subspace selection. Neurocomputing 71(10):1866–1882
Wang C, Zhang J, Pu J, Yuan X, Wang L (2010) Chrono-gait image: a novel temporal template for gait recognition. Comput Vis ECCV 2010(6311):257–270
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, pp 257–270
Wang L, Tan T, Hu W, Ning H (2003) Automatic gait recognition based on statistical shape analysis. IEEE Trans Image Process 12:1120–1131
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
Xu D, Yan S, Tao D, Lin S, Zhang HJ (2007) Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval. IEEE Trans Image Process 16(11):2811–2821
Xu D, Yan S, Tao D, Zhang L, Li X, Zhang HJ (2006) Human gait recognition with matrix representation. IEEE Trans Circuits Syst Video Technol 16(7):896–903
Xu D, Yan S, Zhang L, Zhang HJ, Liu Z, Shum HY (2005) Concurrent subspaces analysis. In: IEEE computer society conference on computer vision and pattern recognition, vol 2. IEEE, pp 203–208
Xu R, Chen YW (2009) Generalized N-dimensional principal component analysis (GND-PCA) and its application on construction of statistical appearance models for medical volumes with fewer samples. Neurocomputing 72(10):2276–2287
Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang HJ (2005) Discriminant analysis with tensor representation. In: IEEE computer society conference on computer vision and pattern recognition, pp 526–532
Yang W, Wang J, Ren M, Yang J, Zhang L, Liu G (2009) Feature extraction based on Laplacian bidirectional maximum margin criterion. Pattern Recognit 42(11):2327–2334
Ye J (2005) Generalized low rank approximations of matrices. Mach Learn 61(1–3):167–191
Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: International Conference on Pattern Recognition, pp 441–444
Zhang W, Lin Z, Tang X (2009) Tensor linear Laplacian discrimination (TLLD) for feature extraction. Pattern Recognit 42:1941–1948
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chhatrala, R., Patil, S., Lahudkar, S. et al. Sparse multilinear Laplacian discriminant analysis for gait recognition. Pattern Anal Applic 22, 505–518 (2019). https://doi.org/10.1007/s10044-017-0648-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-017-0648-1