A View Transformation Model Based on Sparse and Redundant Representation for Human Gait Recognition
DOI: 10.4103/jmss.JMSS_59_19
Abstract
Background: Human gait as an effective behavioral biometric identifier has received much attention in recent years. However, there are challenges which reduce its performance. In this work we aim at improving performance of gait systems under variations in view angles, which present one of the major challenges to gait algorithms. Methods: We propose employment of a view transformation model based on sparse and redundant (SR) representation. More specifically, our proposed method trains a set of corresponding dictionaries for each viewing angle, which are then used in identification of a probe. In particular, the view transformation is performed by first obtaining the SR representation of the input image using the appropriate dictionary, then multiplying this representation by the dictionary of destination angle to obtain a corresponding image in the intended angle. Results: Experiments performed using CASIA Gait Database, Dataset B, support the satisfactory performance of our method. It is observed that in most tests, the proposed method outperforms the other methods in comparison. This is especially the case for large changes in the view angle, as well as the average recognition rate. Conclusion: A comparison with state-of-the-art methods in the literature showcases the superior performance of the proposed method, especially in the case of large variations in view angle.
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Larsen PK, Simonsen EB, Lynnerup N. Gait analysis in forensic medicine. J Forensic Sci 2008;53:1149-53.
BBC. How can you Identify a Criminal by the way they Walk? BBC Magazine; 2008. Available from: http://news.bbc.co.uk/1/hi/magazine/7348164.stm. [Last accessed on 2019 Oct 20].
Kusakunniran W, Wu Q, Zhang J, Li H. Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans Circuits Syst Video Technol 2012;22:966-80.
Kovac J, struc V, Peer P. Frame–based classification for cross-speed gait recognition. Multimed Tools Appl 2019;78:5621-43.
Ghebleh A, Moghaddam ME. Clothing-invariant human gait recognition using an adaptive outlier detection method. Multimed Tools Appl 2018:77;8237-57.
Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW. The humanID gait challenge problem: Data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 2005;27:162-77.
Yu S, Tan D, Tan T. Modelling the effect of view angle variation on appearance-based gait recognition. In: Asian Conference on Computer Vision. Springer, Berlin, Heidelberg; 2006. p. 807-816.
Bobick AF, Johnson AY, editors. Gait recognition using static, activity-specific parameters. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE; 2001.
Cunado D, Nixon MS, Carter JN. Automatic extraction and description of human gait models for recognition purposes. Comput Vis Image Underst 2003;90:1-41.
Zhang R, Vogler C, Metaxas D. Human gait recognition at sagittal plane. Image Vis Comput 2007;25:321-30.
Wang L, Ning H, Tan T, Hu W. Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans Circuits Syst Video Technol 2004;14:149-58.
Dockstader SL, Berg MJ, Tekalp AM. Stochastic kinematic modeling and feature extraction for gait analysis. IEEE Trans Image Process 2003;12:962-76.
Haiping L, Konstantinos NP, Anastasios NV. A full-body layered deformable model for automatic model-based gait recognition. EURASIP J Adv Signal Process 2007;2008:1-13.
Zhao G, Liu G, Li H, Pietikainen M, editors. 3D gait recognition using multiple cameras. In: 7th International Conference on Automatic Face and Gesture Recognition. IEEE; 2006.
Ariyanto G, Nixon MS, editors. Model-based 3D gait biometricsInternational Joint Conference on Biometrics. IEEE; 2011.
Bodor R, Drenner A, Fehr D, Masoud O, Papanikolopoulos N. View-independent human motion classification using image-based reconstruction. Image Vis Comput 2009;27:1194-206.
Han J, Bhanu B. Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 2006;28:316-22.
Wang L, Tan T, Ning H, Hu W. Silhouette analysis-based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 2003;25:1505-18.
Bobick AF, Davis JW. The recognition of human movement using temporal templates. IEEE Trans Pattern Anal Mach Intell 2001;23:257-67.
Liu J, Zheng N, editors. Gait history image: A novel temporal template for gait recognition. In: IEEE International Conference on Multimedia and Expo. IEEE; 2007.
Lam TH, Cheung KH, Liu JN. Gait flow image: A silhouette-based gait representation for human identification. Pattern Recognit 2011;44:973-87.
Yang X, Zhou Y, Zhang T, Shu G, Yang J. Gait recognition based on dynamic region analysis. Signal Process 2008;88:2350-6.
Zhang E, Zhao Y, Xiong W. Active energy image plus 2DLPP for gait recognition. Signal Process 2010;90:2295-302.
Bashir K, Xiang T, Gong S. Gait Recognition Using Gait Entropy Image; 2009.
LI X, Chen Y. Gait recognition based on structural gait energy image. J Comput Inf Syst 2013;9:121-6.
Goffredo M, Bouchrika I, Carter JN, Nixon MS. Self-calibrating view-invariant gait biometrics. IEEE Trans Syst Man Cybern B Cybern 2010;40:997-1008.
Kusakunniran W, Wu Q, Zhang J, Ma Y, Li H. A new view-invariant feature for cross-view gait recognition. IEEE Trans Inf Forensics Secur 2013;8:1642-53.
Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y, editors. Gait recognition using a view transformation model in the frequency domain. In: European Conference on Computer Vision. Springer; 2006.
Kusakunniran W, Wu Q, Zhang J, Li H, Wang L. Recognizing gaits across views through correlated motion co-clustering. IEEE Trans Image Process 2014;23:696-709.
Bashir K, Xiang T, Gong S, Mary Q, editors. Gait Representation Using Flow Fields. BMVC; 2009.
Arora P, Hanmandlu M, Srivastava S. Gait based authentication using gait information image features. Pattern Recognit Lett 2015;68:336-42.
Chai Y, Ren J, Han W, Li H. Human Gait Recognition: Approaches, Datasets and Challenges; 2011.
Muramatsu D, Makihara Y, Yagi Y. View transformation model incorporating quality measures for cross-view gait recognition. IEEE Trans Cybern 2016;46:1602-15.
Shakhnarovich G, Lee L, Darrell T, editors. Integrated face and gait recognition from multiple views. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE; 2001.
Zhang Z, Troje NF. View-independent person identification from human gait. Neurocomputing 2005;69:250-6.
Lopez-Fernandez D, Madrid-Cuevas FJ, Carmona-Poyato A, Munoz-Salinas R, Medina-Carnicer R. A new approach for multi-view gait recognition on unconstrained paths. J Vis Commun Image Represent 2016;38:396-406.
Goffredo M, Seely RD, Carter JN, Nixon MS, editors. Markerless view independent gait analysis with self-camera calibration. In: 8th IEEE International Conference on Automatic Face & Gesture Recognition. IEEE; 2008.
Iwashita Y, Baba R, Ogawara K, Kurazume R, editors. Person identification from spatio-temporal 3D gait. In: International Conference on Emerging Security Technologies (EST). IEEE; 2010.
Zhaoxiang Zhang, Jiaxin Chen, Qiang Wu, Ling Shao. GII representation-based cross-view gait recognition by discriminative projection with list-wise constraints. IEEE Trans Cybern 2018;48:2935-47.
Seely RD, Samangooei S, Lee M, Carter JN, Nixon MS, editors. The university of southampton multi-biometric tunnel and introducing a novel 3d gait dataset. In: 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems. IEEE; 2008.
BenAbdelkader C, Cutler RG, Davis LS. Gait recognition using image self-similarity. EURASIP J Adv Signal Process 2004;2004:572-85.
Kale A, Chowdhury AR, Chellappa R, editors. Towards a view invariant gait recognition algorithm. In: Proceedings IEEE Conference on Advanced Video and Signal Based Surveillance. IEEE; 2003.
Jean F, Bergevin R, Albu AB, editors. Trajectories normalization for viewpoint invariant gait recognition. In: 19th International Conference on Pattern Recognition. IEEE; 2008.
Jean F, Bergevin R, Albu AB. Computing and evaluating view-normalized body part trajectories. Image Vis Comput 2009;27:1272-84.
Han J, Bhanu B, Roy-Chowdhury AK, editors. A study on view-insensitive gait recognition. In: IEEE International Conference on Image Processing. IEEE; 2005.
Liu N, Lu J, Tan YP. Joint subspace learning for view-invariant gait recognition. IEEE Signal Process Lett 2011;18:431-4.
Kusakunniran W, Wu Q, Li H, Zhang J, editors. Multiple views gait recognition using view transformation model based on optimized gait energy image. In: IEEE 12th International Conference on Computer Vision Workshops. IEEE; 2009.
Zheng S, Zhang J, Huang K, He R, Tan T, editors. Robust view transformation model for gait recognition. In: 18th IEEE International Conference on Image Processing; 2011: IEEE; 2011.
Kusakunniran W, Wu Q, Zhang J, Li H, editors. Support vector regression for multi-view gait recognition based on local motion feature selection. In: CVPR, 2010: IEEE Conference on Computer Vision and Pattern Recognition; IEEE; 2010.
Kusakunniran W, Wu Q, Zhang J, Li H. Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron. Pattern Recognit Lett 2012;33:882-9.
Chen X, Yang T, Xu J. Cross-view gait recognition based on human walking trajectory. J Vis Commun Image Represent 2014;25:1842-55.
Liu N, Tan YP, editors. View invariant gait recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing; 2010.
Bashir K, Xiang T, Gong S, editors. Cross View Gait Recognition Using Correlation Strength. BMVC; 2010.
Hu M, Wang Y, Zhang Z, Little JJ, Huang D. View-invariant discriminative projection for multi-view gait-based human identification. IEEE Trans Inf Forensics Secur 2013;8:2034-45.
Hu H. Enhanced Gabor feature based classification using a regularized locally tensor discriminant model for multiview gait recognition. IEEE Transactions on Circuits and Systems Video Technol 2013;23:1274-86.
Muramatsu D, Shiraishi A, Makihara Y, Uddin MZ, Yagi Y. Gait-based person recognition using arbitrary view transformation model. IEEE Trans Image Process 2015;24:140-54.
Muramatsu D, Shiraishi A, Makihara Y, Yagi Y, editors. Arbitrary view transformation model for gait person authentication. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS). IEEE; 2012.
Wu Z, Huang Y, Wang L, Wang X, Tan T. A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs. IEEE Trans Pattern Anal Mach Intell 2017;39:209-26.
Tian Y, Wei L, Lu S, Huang T. Free-view gait recognition. PLoS One 2019;
Yu S, Tan D, Tan T, editors. A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition. 18th International Conference on Pattern Recognition. IEEE; 2006.
Piccardi M, editor. Background Subtraction Techniques: A Review. IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat No 04CH37583). IEEE; 2004.
Chtourou I, Fendri E, Hammami M. Walking direction estimation for gait based applications. Procedia Comput Sci 2018;126:759-67.
Aharon M, Elad M, Bruckstein A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 2006;54:4311-22.
Elad M. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Berlin: Springer Science & Business Media; 2010.
Rubinstein R, Zibulevsky M, Elad M. Efficient Implementation of the K-SVD Algorithm and the Batch-OMP Method. Department of Computer Science, Technion, Israel, Technology Report; 2008.
Phillips PJ, Grother PJ, Micheals RJ, Blackburn DM, Tabassi E, Bone M. Face Recognition Vendor Test 2002: Evaluation Report; 2003.
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