Abstract
This paper proposes a new gait representation that encodes the dynamics of a gait period through a 2D array of 17-bin histograms. Every histogram models the co-occurrence of optical flow states at every pixel of the normalized template that bounds the silhouette of a target subject. Five flow states (up, down, left, right, null) are considered. The first histogram bin counts the number of frames over the gait period in which the optical flow for the corresponding pixel is null. In turn, each of the remaining 16 bins represents a pair of flow states and counts the number of frames in which the optical flow vector has changed from one state to the other during the gait period. Experimental results show that this representation is significantly more discriminant than previous proposals that only consider the magnitude and instantaneous direction of optical flow, especially as the walking direction gets closer to the viewing direction, which is where state-of-the-art gait recognition methods yield the lowest performance. The dimensionality of that gait representation is reduced through principal component analysis. Finally, gait recognition is performed through supervised classification by means of support vector machines. Experimental results using the public CMU MoBo and AVAMVG datasets show that the proposed approach is advantageous over state-of-the-art gait representation methods.
Similar content being viewed by others
References
Bazin, A.I., Nixon, M.S.: Gait verification using probabilistic methods, In: Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION’05) - Volume 1 - Volume 01, series WACV-MOTION ’05, pp. 60–65. IEEE Computer Society, Washington, DC, 2005. https://doi.org/10.1109/ACVMOT.2005.55
He, W., Li, P.: Gait recognition using the temporal information of leg angles, In: 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), 2010, vol. 5, pp. 78–83 (2010)
Choudhury, S.D., Tjahjadi, T.: Gait recognition based on shape and motion analysis of silhouette contours. Comput. Vis. Image Underst. 117(12), 1770–1785 (2013)
Kovac, J., Peer, P.: Human skeleton model based dynamic features for walking speed invariant gait recognition. Math. Prob. Eng. 2014, 1 (2014)
Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23, 257–267 (2001)
Lee, C.P., Tan, A.W., Tan, S.C.: Time-sliced averaged motion history image for gait recognition. J. Vis. Commun. Image Represent. 25(5), 822–826 (2014)
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)
Hosseini, N.K., Nordin, M.J.: Human gait recognition: A silhouette based approach. J. Autom. Control Eng. 1(2), 103–105 (2013)
Tan, D., Huang, K., Yu, S., Tan, T.: Efficient night gait recognition based on template matching. In: Proceedings of the 18th International Conference on Pattern Recognition - Volume 03, series ICPR ’06, pp. 1000–1003. IEEE Computer Society, Washington, DC (2006). https://doi.org/10.1109/ICPR.2006.478
Tao, D., Li, X., Wu, X., Maybank, S.J.: General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1700–1715 (2007)
Hayder Ali, C.A.E.G.M., Dargham, J.: C.A.E.G.M., Dargham, Jamal: Gait recognition using gait energy image. Int. J. Signal Process. Image Proc. Pattern Recognit. 4, 3.141–3.152 (2011)
Tee, C., Goh, M., Teoh, A.: Gait recognition using sparse grassmannian locality preserving discriminant analysis, In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, pp. 2989–2993 (2013)
Kusakunniran, W., Wu, Q., Li, H., Zhang, J.: Automatic gait recognition using weighted binary pattern on video. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009. AVSS ’09, pp. 49–54 (2009)
Lishani, A.O., Boubchir, L., Khalifa, E., Bouridane, A.: Human gait recognition using GEI-based local multi-scale feature descriptors. Multimed. Tools Appl. 77, 1–16 (2018)
Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2164–2176 (2012)
Tang, J., Luo, J., Tjahjadi, T., Guo, F.: Robust arbitrary-view gait recognition based on 3d partial similarity matching. IEEE Trans. Image Process. 26(1), 7–22 (2017)
Jia, N., Li, C.-T., Sanchez, V., Liew, A.W.-C.: Fast and robust framework for view-invariant gait recognition. In: 5th International Workshop on Biometrics and Forensics (IWBF), 2017, pp. 1–6. IEEE (2017)
BenAbdelkader, C., Cutler, R., Nanda, H., Davis, L.S.: Eigengait: Motion-based recognition of people using image self-similarity. In: Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication, series AVBPA ’01, pp. 284–294. Springer, London (2001). http://dl.acm.org/citation.cfm?id=646073.677457
Bashir, K., Xiang, T., Gong, S.: Gait representation using flow fields. In: Proceedings of the British Machine Vision Conference, pp. 113.1–113.11. BMVA Press (2009)
Lam, T.H.W., Cheung, K.H., Liu, J.N.K.: Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit. 44(4), 973–987 (2011)
Castro, F.M., Marín-Jimenez, M.J., Medina-Carnicer, R.: Pyramidal fisher motion for multiview gait recognition. In: Proceedings of the 2014 22Nd International Conference on Pattern Recognition, series ICPR ’14, pp. 1692–1697. IEEE Computer Society, Washington, DC (2014). https://doi.org/10.1109/ICPR.2014.298
Mahfouf, Z., Bouchrika, I., Merouani, H.F., Harrati, N.: Gait biometrics via optical flow motion features for people identification. In: 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2016, pp. 312–321. IEEE (2016)
Mahfouf, Z., Merouani, H.F., Bouchrika, I., Harrati, N.: Investigating the use of motion-based features from optical flow for gait recognition. Neurocomputing 283, 140–149 (2018)
Laptev, I., Marszaek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)
Wang, H., Kläser, A., Schmid, C., Liu, C.-L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)
Peng, X., Qiao, Y., Peng, Q.: Motion boundary based sampling and 3d co-occurrence descriptors for action recognition. Image Vis. Comput. 32(9), 616–628 (2014)
Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27, 162–177 (2005)
Rashwan, H.A., García, M.A., Puig, D.: Variational optical flow estimation based on stick tensor voting. IEEE Trans. Image Process. 22(7), 2589–2599 (2013)
Lee, H., Hong, S., Kim, E.: An efficient gait recognition with backpack removal. EURASIP J. Adv. Signal Process 2009, 4.61–4.67 (2009). https://doi.org/10.1155/2009/384384
Gross, R., Shi, J.: The CMU motion of body (mobo) database. Robotics Institute, Pittsburgh, PA, Technical Report CMU-RI-TR-01-18 (2001)
Lopez-Fernandez, A.C.P.M.M.-J.D., Madrid-Cuevas, F.J., Muoz-Salinas, R.: The AVA multi-view dataset for gait recognition (AVAMVG) In: International Workshop on Activity Monitoring by Multiple Distributed Sensing (AMMDS) (2014)
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Each of the 17-bin histograms models a co-occurrence of optical flow states between a pair of templates separated by m frames, \(T_i\) and \(T_j\), where \(j = (i+m)\%n\), \(0< m < n\) and \(0\le i,j < n\), with n being the number of frames in a gait period. Those bins are noted as: \(HV _i\), \(HR _i\), \(HL _i\), \(LR _i\), \(LL _i\), \(LH _i\), \(RR _i\), \(RL _i\), \(RH _i\), \(VU _i\), \(VD _i\), \(UU _i\), \(UD _i\), \(UV _i\), \(DU _i\), \(DD _i\), \(DV _i\).
Finally, an accumulation \(h \times w\) histogram of 17 bins is computed. The 17 bins can be defined as:
Rights and permissions
About this article
Cite this article
Rashwan, H.A., García, M.Á., Chambon, S. et al. Gait representation and recognition from temporal co-occurrence of flow fields. Machine Vision and Applications 30, 139–152 (2019). https://doi.org/10.1007/s00138-018-0982-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-018-0982-3