Abstract
Gait refers to the walking style of a person and it has emerged as an important biometric feature for person identification. The gait recognition algorithms proposed in literature exploit various types of information from the gait video sequence, such as, the skeletal data, human body shape, and silhouettes; and use these features to recognize the individuals. This paper presents the proposal of using a generic codebook in gait recognition. The idea is built upon a novel gait representation which exploits the spatiotemporal motion characteristics of the individual for identification. In particular, we propose to use a set of sample gait sequences to construct a generic codebook and use it to build a gait signature for person identification. To this end, we chose synthetic gait sequences of CMU MoCap gait database due to its diversity in walking styles. A set of spatiotemporal features are extracted from these sequences to build a generic codebook. The motion descriptors of real gait sequences are encoded using this generic codebook and Fisher vector encoding; the classification is performed using support vector machine. An extensive evaluation of this novel proposal is carried out using five benchmark gait databases: NLPR, CMU MoBo, TUM GAID, CASIA-B, and CASISA-C. In all experiments, the generic codebook is used in feature encoding. The performance of the proposed algorithm is also compared with the state-of-the-art gait recognition techniques and the results show that the idea of using a generic codebook in gait recognition is practical and effective.
Similar content being viewed by others
References
Anzai Y (2012) Pattern recognition and machine learning. Elsevier, Amsterdam
Bashir K, Xiang T, Gong S (2008) Feature selection for gait recognition without subject cooperation. In: BMVC, pp 1–10
Bashir K, Xiang T, Gong S (2009) Gait recognition using gait entropy image. In: IET ICDP, pp 1–6
Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recognit Lett 31(13):2052–2060
Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: ECCV 2006, pp 404–417
BenAbdelkader C, Cutler RG, Davis LS (2004) Gait recognition using image self-similarity. EURASIP J Adv Signal Process 2004(4):1–14
Bouchrika I, Carter JN, Nixon M (2016) Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras. Multimed Tools Appl 75(2):1201–1221
Bouchrika I, Nixon M (2007) Model-based feature extraction for gait analysis and recognition. In: ICCV. Springer, pp 150–160
Castro FM, Marín-Jiménez MJ, Mata NG, Muñoz-Salinas R (2017) Fisher motion descriptor for multiview gait recognition. Int J Pattern Recognit Artif Intell 31(01):1756002. https://doi.org/10.1142/S021800141756002X. http://www.worldscientific.com/doi/abs/10.1142/S021800141756002X
Castro FM, Marín-jiménez MJ, Guil N, de la Blanca NP (2017) Automatic learning of gait signatures for people identification. In: International work-conference on artificial neural networks. Springer, pp 257–270
Chai Y, et al. (2006) A novel human gait recognition method by segmenting and extracting the region variance feature. In: Proc. Int. Conf. Pattern Recognit. (ICPR), vol 4, pp 425–428
Chen C, et al. (2009) Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognit Lett 30(11):977–984
Chen S, Gao Y (2007) An invariant appearance model for gait recognition. In: Proc. IEEE Int. Conf. Multimed. and expo (ICME). IEEE, pp 1375–1378
Choudhury SD, Tjahjadi T (2012) Silhouette-based gait recognition using procrustes shape analysis and elliptic fourier descriptors. Pattern Recognit 45 (9):3414–3426
CMU motion capture database. http://mocap.cs.cmu.edu/
Cunado D, Nixon M, Carter JN (2003) Automatic extraction and description of human gait models for recognition purposes. Comput Vis Image Underst 90(1):1–41
Dadashi F, et al. (2009) Gait recognition using wavelet packet silhouette representation and transductive support vector machines. In: 2nd Int. congress on image and signal process, pp 1–5
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE CVPR, vol 1, pp 886–893. https://doi.org/10.1109/CVPR.2005.177
Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: ECCV, pp 428–441
DeCann B, Ross A (2010) Gait curves for human recognition, backpack detection, and silhouette correction in a nighttime environment. In: SPIE defense, security, and sensing. International society for optics and photonics, pp 76670q–76670q
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B-Stat Methodol 1977:1–38
Dupuis Y, Savatier X, Vasseur P (2013) Feature subset selection applied to model-free gait recognition. Image Vis Comput 31(8):580–591
Fan RE, et al. (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9(Aug):1871–1874
Geng X, Wang L, Li M, Wu Q, Smith-Miles K (2007) Distance-driven fusion of gait and face for human identification in video. In: Image and vision computing conference. Image and vision computing New Zealand
Goffredo M, Bouchrika I, Carter JN, Nixon M (2010) Performance analysis for automated gait extraction and recognition in multi-camera surveillance. Multimed. Tools Appl. 50(1):75–94
Goffredo M, Carter JN, Nixon M (2008) Front-view gait recognition. In: IEEE Int. Conf. Biometrics: theory, Appl. and Systems (BTAS). IEEE, pp 1–6
Gross R, Shi J (2001) The CMU motion of body (mobo) database. Gait Video Sequences
Hofmann M, Bachmann S, Rigoll G (2012) 2.5D gait biometrics using the depth gradient histogram energy image. In: IEEE BATS Conf., pp 399–403
Hu M, Wang Y, Zhang Z (2013) Cross-view gait recognition with short probe sequences: from view transformation model to view-independent stance-independent identity vector. Int J Pattern Recognit Artif Intell 27 (06):1350017. https://doi.org/10.1142/S0218001413500171. http://www.worldscientific.com/doi/abs/10.1142/S0218001413500171
Hu M, Wang Y, Zhang Z, Zhang D, Little JJ (2013) Incremental learning for video-based gait recognition with lbp flow. IEEE Trans Cybern 43(1):77–89
Kale A, Cuntoor N, Yegnanarayana B, Rajagopalan A, Chellappa R (2003) Gait analysis for human identification. In: Int. Conf. on audio-and video-based biometric person authentication. Springer, pp 706–714
Kale A, Sundaresan A, Rajagopalan A, Cuntoor NP, Roy-Chowdhury AK, Kruger V, Chellappa R (2004) Identification of humans using gait. IEEE Trans Image Process 13(9):1163–1173
Khan M, et al. (2016) Automatic recognition of movement patterns in the vojta-therapy using rgb-d data. In: Proc. Int. Conf. Image Process. (ICIP), pp 1235–1239
Khan MH (2018) Human activity analysis in visual surveillance and healthcare, vol 45. Logos Verlag Berlin GmbH, Berlin
Khan MH, Farid MS, Grzegorzek M (2017) Person identification using spatiotemporal motion characteristics. In: Proc. Int. Conf. Image Process. (ICIP). IEEE, pp 166–170
Khan MH, Farid MS, Grzegorzek M (2018) Using a generic model for codebook-based gait recognition algorithms. In: Int. workshop biometrics forensics (IWBF). IEEE, pp 1–7
Khan MH, Farid MS, Grzegorzek M (2019) Spatiotemporal features of human motion for gait recognition. Signal Image Video Process 13(2):369–377
Khan MH, Farid MS, Zahoor M, Grzegorzek M (2018) Cross-view gait recognition using non-linear view transformations of spatiotemporal features. In: Proc. Int. Conf. Image Process. (ICIP). IEEE, pp 773–777
Khan MH, Helsper J, Farid MS, Grzegorzek M (2018) A computer vision-based system for monitoring vojta therapy. J Med Informat 113:85–95
Khan MH, Li F, Farid MS, Grzegorzek M Kurzynski M, Wozniak M, Burduk R (eds) (2017) Gait recognition using motion trajectory analysis. Springer, Cham
Kusakunniran W (2014) Attribute-based learning for gait recognition using spatio-temporal interest points. Image Vis Comput 32(12):1117–1126
Kusakunniran W, Wu Q, Li H, Zhang J (2009) Automatic gait recognition using weighted binary pattern on video. In: IEEE Int. Conf. Adv. video signal based surveillance (AVSS). IEEE, pp 49–54
Kusakunniran W, Wu Q, Zhang J, Li H (2011) Pairwise shape configuration-based psa for gait recognition under small viewing angle change. In: IEEE Int. Conf. Adv. Video signal based surveillance (AVSS). IEEE, pp 17–22
Laptev I, Marszalek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: IEEE CVPR, pp 1–8, DOI https://doi.org/10.1109/CVPR.2008.4587756
Lee H, Hong S, Kim E (2008) An efficient gait recognition based on a selective neural network ensemble. Int J Imaging Syst Technol 18(4):237–241
Lee L, Grimson WEL (2002) Gait analysis for recognition and classification. In: Proc. Int. Conf. Automatic face and gesture recognit. IEEE, pp 155–162
Liang J, Chen Y, Hu H, Zhao H (2006) Appearance-based gait recognition using independent component analysis. In: Int. Conf. on natural computation. Springer, pp 371–380
Loula F, Prasad S, Harber K, Shiffrar M (2005) Recognizing people from their movement. J Exp Psychol-Hum Percept 31(1):210
Lowe DG (1999) Object recognition from local scale-invariant features. In: EEE ICCV, vol 2, pp 1150–1157 vol.2. https://doi.org/10.1109/ICCV.1999.790410
Lu J, Zhang E, Jing C (2006) Gait recognition using wavelet descriptors and independent component analysis. In: Int. Symp. Neural networks. Springer, pp 232–237
Lun R, Zhao W (2015) A survey of applications and human motion recognition with microsoft kinect. Int J Pattern Recognit Artif Intell 29 (05):1555008. https://doi.org/10.1142/S0218001415550083. http://www.worldscientific.com/doi/abs/10.1142/S0218001415550083
Man J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322
Nixon M, et al. (2009) Model-based gait recognition. In: Enclycopedia of biometrics. Springer, pp 633–639. https://eprints.soton.ac.uk/268238/
Peng X, Wang L, Wang X, Qiao Y (2016) Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. Comput Vis Image Underst 150:109–125
Perronnin F, Sánchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In: ECCV. Springer, pp 143–156
Rokanujjaman M, Islam MS, Hossain MA, Islam MR, Makihara Y, Yagi Y (2015) Effective part-based gait identification using frequency-domain gait entropy features. Multimed Tools Appl 74(9):3099–3120
Samangooei S, Nixon M (2010) Performing content-based retrieval of humans using gait biometrics. Multimed Tools Appl 49(1):195–212
Sánchez J, et al. (2013) Image classification with the fisher vector: theory and practice. Int J Comput Vis 105(3):222–245
Shaikh SH, Saeed K, Chaki N (2014) Gait recognition using partial silhouette-based approach. In: Int. Conf. Signal process. and integrated netw. (SPIN). IEEE, pp 101–106
Sivapalan S, Chen D, Denman S, Sridharan S (2011) Fookes, c.: 3d ellipsoid fitting for multi-view gait recognition. In: IEEE Int. Conf. Adv. Video signal based surveillance (AVSS). IEEE, pp 355–360
Su H, Huang F (2006) Gait recognition using principal curves and neural networks. In: Int. Symp. Neural Networks. Springer, pp 238–243
Sun C, Nevatia R (2013) Large-scale web video event classification by use of fisher vectors. In: IEEE Int. Workshop Appl. Comput. Vis. (WACV). IEEE, pp 15–22
Tan D, Huang K, Yu S, Tan T (2007) Orthogonal diagonal projections for gait recognition. In: Proc. Int. Conf. Image process. (ICIP), vol 1. IEEE, pp i–337
Tan D, Huang K, Yu S, Tan T (2007) Recognizing night walkers based on one pseudoshape representation of gait. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern recognit. (CVPR). IEEE, pp 1–8
Tan D, Huang K, Yu S, Tan T (2007) Uniprojective features for gait recognition. In: Int. Conf. Biometrics (ICB). Springer, pp 673–682
Tan D, Yu S, Huang K, Tan T (2007) Walker recognition without gait cycle estimation. In: Int. Conf. on biometrics, pp 222–231
Tan D, et al. (2006) Efficient night gait recognition based on template matching. In: Proc. Int. Conf. Pattern recognit. (ICPR), vol 3, pp 1000–1003
Veeraraghavan A, Chowdhury AR, Chellappa R (2004) Role of shape and kinematics in human movement analysis. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), vol 1. IEEE, pp i–730
Veeraraghavan A, Roy-Chowdhury AK, Chellappa R (2005) Matching shape sequences in video with applications in human movement analysis. IEEE Trans Pattern Anal Mach Intell 27(12):1896–1909
Wan M, Lai Z, Yang G, Yang Z, Zhang F, Zheng H (2017) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Sets Syst 318:120–131
Wan M, Li M, Yang G, Gai S, Jin Z (2014) Feature extraction using two-dimensional maximum embedding difference. Information Science 274:55–69
Wan M, Yang G, Gai S, Yang Z (2017) Two-dimensional discriminant locality preserving projections (2ddlpp) and its application to feature extraction via fuzzy set. Multimed Tools Appl 76(1):355–371
Wang C, et al. (2012) Human identification using temporal information preserving gait template. IEEE Trans Pattern Anal Mach Intell 34(11):2164–2176
Wang H, Schmid C (2013) Action recognition with improved trajectories. In: IEEE ICCV, pp 3551–3558
Wang L, Ning H, Tan T, Hu W (2004) Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans Circuits Syst Video Technol 14(2):149–158
Wang L, Tan T, Hu W, Ning H (2003) Automatic gait recognition based on statistical shape analysis. IEEE Trans Image Process 12(9):1120–1131
Wang L, Tan T, Ning H, Hu W (2003) Silhouette analysis-based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25 (12):1505–1518
Whytock T, Belyaev A, Robertson N (2014) Dynamic distance-based shape features for gait recognition. J Math Imaging Vis 50(3):314–326
Wu Q, Wang L, Geng X, Li M, He X (2007) Dynamic biometrics fusion at feature level for video based human recognition. In: Proc. of image and Vis. Computing New Zealand. Citeseer, pp 152–157
Yang Y, Tu D, Li G (2014) Gait recognition using flow histogram energy image. In: Proc. Int. Conf. Pattern recognit. (ICPR), pp 444–449
Yu S, et al. (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proc. Int. Conf. Pattern recognit. (ICPR), vol 4, pp 441–444
Zeng W, Wang C, Yang F (2014) Silhouette-based gait recognition via deterministic learning. Pattern Recognit 47(11):3568–3584
Zhang E, Zhao Y, Xiong W (2010) Active energy image plus 2dlpp for gait recognition. Signal Process 90(7):2295–2302
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Khan, M.H., Farid, M.S. & Grzegorzek, M. A generic codebook based approach for gait recognition. Multimed Tools Appl 78, 35689–35712 (2019). https://doi.org/10.1007/s11042-019-08007-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-08007-z