[1] |
Phillips P J. Human identification technical challenges. In: Proceedings of the 2002 International Conference on Image Processing. New York, USA: IEEE, 2002. I-49-I-52 |
[2] |
Tariq M, Shah M A. Review of model-free gait recognition in biometrie systems. In: Proceedings of the 23rd International Conference on Automation and Computing. Huddersfield, UK: IEEE, 2017. 1-7 |
[3] |
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 Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2):162-177 doi: 10.1109/TPAMI.2005.39 |
[4] |
Liu Z, Sarkar S. Effect of silhouette quality on hard problems in gait recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2005, 35(2):170-183 doi: 10.1109/TSMCB.2004.842251 |
[5] |
Kale A, Sundaresan A, Rajagopalan A N, Cuntoor N P, Roy-Chowdhury A K, Kruger V, et al. Identification of humans using gait. IEEE Transactions on Image Processing, 2004, 13(9):1163-1173 doi: 10.1109/TIP.2004.832865 |
[6] |
Masood H, Farooq H. A proposed framework for vision based gait biometric system against spoofing attacks. In: Proceedings of the 2017 International Conference on Communication, Computing and Digital Systems. Islamabad, Pakistan: IEEE, 2017. 357-362 |
[7] |
Arseneau S, Cooperstock J R. Real-time image segmentation for action recognition. In: Proceedings of the 1999 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. British Columbia, Canada: IEEE, 1999. 86-89 |
[8] |
Verlekar T T, Correia P L, Soares L D. View-invariant gait recognition system using a gait energy image decomposition method. IET Biometrics, 2017, 6(4):299-306 doi: 10.1049/iet-bmt.2016.0118 |
[9] |
Niyogi S A, Adelson E H. Analyzing gait with spatiotemporal surfaces. In: Proceedings of the 1994 IEEE Workshop on Motion of Non-Rigid and Articulated Objects. Texas, USA: IEEE, 1994. 64-69 |
[10] |
王科俊, 侯本博.步态识别综述.中国图象图形学报, 2007, 12(7):1152-1160 doi: 10.3969/j.issn.1006-8961.2007.07.002
Wang Ke-Jun, Hou Ben-Bo. A survey of gait recognition. Journal of Image and Graphics, 2007, 12(7):1152-1160 doi: 10.3969/j.issn.1006-8961.2007.07.002 |
[11] |
Sugandhi K, Wahid F F, Raju G. Feature extraction methods for human gait recognition——a survey. In: Proceedings of the 2017 Advances in Computing and Data Sciences. Communications in Computer and Information Science, vol. 721. Singapore: Springer, 2017. 377-385 |
[12] |
Lv Z W, Xing X L, Wang K J, Guan D H. Class energy image analysis for video sensor-based gait recognition:a review. Sensors, 2015, 15(1):932-964 |
[13] |
Wang L, Tan T N, Ning H Z, Hu W M. Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12):1505-1518 doi: 10.1109/TPAMI.2003.1251144 |
[14] |
Yu S Q, Tan D L, Tan T N. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proceedings of 18th International Conference on Pattern Recognition. Hong Kong, China: IEEE, 2006. 441-444 |
[15] |
Chalidabhongse T, Kruger V, Chellappa R. The UMD Database for Human Identification at a Distance, Technical Report, University of Maryland, USA, 2001 |
[16] |
Gross R, Shi J B. The CMU Motion of Body (MoBo) Database, Technical Report CMU-RI-TR-01-18, Carnegie Mellon University, USA, 2001 |
[17] |
Makihara Y, Mannami H, Yagi Y. Gait analysis of gender and age using a large-scale multi-view gait database computer vision. In: Proceedings of Asian Conference on Computer Vision. Queenstown, New Zealand: ACM, 2010. 440-451 |
[18] |
Iwama H, Okumura M, Makihara Y, Yagi Y. The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Transactions on Information Forensics and Security, 2012, 7(5):1511-1521 doi: 10.1109/TIFS.2012.2204253 |
[19] |
Yu S Q, Wang Q, Huang Y Z. A large RGB-D gait dataset and the baseline algorithm. In: Biometric Recognition. Lecture Notes in Computer Science, vol. 8232. Cham: Springer, 2013. 417-424 |
[20] |
Seely R D, Samangooei S, Lee M, Carter J N, Nixon M S. The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset. In: Proceedings of IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems. Virginia USA: IEEE, 2008. 1-6 |
[21] |
Anguelov D, Koller D, Pang H C, Srinivasan P, Thrun S. Recovering articulated object models from 3D range data. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. Banff, Canada: ACM, 2004. 18-26 |
[22] |
López-Fernández D, Madrid-Cuevas F J, Carmona-Poyato A, Muñoz-Salinas R, Medina-Carnicer R. A new approach for multi-view gait recognition on unconstrained paths. Journal of Visual Communication and Image Representation, 2016, 38:396-406 doi: 10.1016/j.jvcir.2016.03.020 |
[23] |
Hofmann M, Sural S, Rigoll G. Gait recognition in the presence of occlusion: a new dataset and baseline algorithms. In: Proceedings of the 19th International Conference in Central Europe Computer Graphics, Visualization and Computer Vision. Plzen, Czech Republic: Václav Skala UNION Agency, 2011. 99-104 |
[24] |
Cho C W, Chao W H, Lin S H, Chen Y Y. A vision-based analysis system for gait recognition in patients with Parkinson's disease. Expert Systems with Applications, 2009, 36(3):7033-7039 doi: 10.1016/j.eswa.2008.08.076 |
[25] |
Stevenage S V, Nixon M S, Vince K. Visual analysis of gait as a cue to identity. Applied Cognitive Psychology, 1999, 13(6):513-526 doi: 10.1002/(ISSN)1099-0720 |
[26] |
Lai D T H, Begg R K, Palaniswami M. Computational intelligence in gait research:a perspective on current applications and future challenges. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(5):687-702 doi: 10.1109/TITB.2009.2022913 |
[27] |
Shakhnarovich G, Lee L, Darrell T. Integrated face and gait recognition from multiple views. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Hawaii, USA: IEEE, 2001. 439-446 |
[28] |
Bodor R, Drenner A, Fehr D, Masoud O, Papanikolopoulos N. View-independent human motion classification using image-based reconstruction. Image and Vision Computing, 2009, 27(8):1194-1206 doi: 10.1016/j.imavis.2008.11.008 |
[29] |
Zhang Z H, Troje N F. View-independent person identification from human gait. Neurocomputing, 2005, 69(1-3):250-256 doi: 10.1016/j.neucom.2005.06.002 |
[30] |
Tang J, Luo J, Tjahjadi T, Gao Y. 2.5D multi-view gait recognition based on point cloud registration. Sensors, 2014, 14(4):6124-6143 doi: 10.3390/s140406124 |
[31] |
Tang J, Luo J, Tjahjadi T, Guo F. Robust arbitrary-view gait recognition based on 3D partial similarity matching. IEEE Transactions on Image Processing, 2017, 26(1):7-22 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=db1c9453b08e2468c550f2b43633a9b4 |
[32] |
Zhao G Y, Liu G Y, Li H, Pietikinen M. 3D gait recognition using multiple cameras. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition. Southampton, UK: IEEE, 2006. 529-534 |
[33] |
Deng M Q, Wang C, Chen Q F. Human gait recognition based on deterministic learning through multiple views fusion. Pattern Recognition Letters, 2016, 78:56-63 doi: 10.1016/j.patrec.2016.04.004 |
[34] |
Deng M Q, Wang C, Cheng F J, Zeng W. Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning. Pattern Recognition, 2017, 67:186-200 doi: 10.1016/j.patcog.2017.02.014 |
[35] |
Iwashita Y, Ogawara K, Kurazume R. Identification of people walking along curved trajectories. Pattern Recognition Letters, 2014, 48:60-69 doi: 10.1016/j.patrec.2014.04.004 |
[36] |
Bobick A F, Davis J W. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(3):257-267 doi: 10.1109/34.910878 |
[37] |
Han J, Bhanu B. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(2):316-323 doi: 10.1109/TPAMI.2006.38 |
[38] |
Lam T H W, Lee R S T. A new representation for human gait recognition: motion silhouettes image (MSI). In: Advances in Biometrics. Lecture Notes in Computer Science, vol. 3832. Berlin, Heidelberg: Springer-Verlag, 2005. 612-618 |
[39] |
Liu J Y, Zheng N N. Gait history image: a novel temporal template for gait recognition. In: Proceedings of the 2007 IEEE International Conference on Multimedia and Expo. Beijing, China: IEEE, 2007. 663-666 |
[40] |
Zhang E H, Zhao Y W, Xiong W. Active energy image plus 2DLPP for gait recognition. Signal Processing, 2010, 90(7):2295-2302 doi: 10.1016/j.sigpro.2010.01.024 |
[41] |
Bashir K, Xiang T, Gong S G. Gait recognition without subject cooperation. Pattern Recognition Letters, 2010, 31(13):2052-2060 doi: 10.1016/j.patrec.2010.05.027 |
[42] |
Chen C H, Liang J M, Zhu X C. Gait recognition based on improved dynamic Bayesian networks. Pattern Recognition, 2011, 44(4):988-995 doi: 10.1016/j.patcog.2010.10.021 |
[43] |
Wang C, Zhang J P, Wang L, Pu J, Yuan X R. Human identification using temporal information preserving gait template. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2164-2176 doi: 10.1109/TPAMI.2011.260 |
[44] |
Jean F, Albu A B, Bergevin R. Towards view-invariant gait modeling:computing view-normalized body part trajectories. Pattern Recognition, 2009, 42(11):2936-2949 doi: 10.1016/j.patcog.2009.05.006 |
[45] |
Ng H, Tan W H, Abdullah J, Tong H L. Development of vision based multiview gait recognition system with MMUGait database. The Scientific World Journal, 2014, 2014:Article ID 376569 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Doaj000003705025 |
[46] |
彭彰, 吴晓娟, 杨军.基于肢体长度参数的多视角步态识别算法.自动化学报, 2007, 33(2):210-213 http://www.aas.net.cn/CN/abstract/abstract13832.shtml
Peng Zhang, Wu Xiao-Juan, Yang Jun. A multi-view method for gait recognition based on the length of body's parts. Acta Automatica Sinica, 2007, 33(2):210-213 http://www.aas.net.cn/CN/abstract/abstract13832.shtml |
[47] |
Goffredo M, Bouchrika I, Carter J N, Nixon M S. Self-calibrating view-invariant gait biometrics. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 40(4):997-1008 doi: 10.1109/TSMCB.2009.2031091 |
[48] |
Lu J W, Wang G, Moulin P. Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Transactions on Information Forensics and Security, 2014, 9(1):51-61 doi: 10.1109/TIFS.2013.2291969 |
[49] |
Darwish S M. Design of adaptive biometric gait recognition algorithm with free walking directions. IET Biometrics, 2017, 6(2):53-60 doi: 10.1049/iet-bmt.2015.0082 |
[50] |
Ma Q Y, Wang S K, Nie D D, Qiu J F. Gait recognition at a distance based on energy deviation image. In: Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering. Wuhan, China: IEEE, 2007. 621-624 |
[51] |
Kale A, Chowdhury A K R, Chellappa R. Towards a view invariant gait recognition algorithm. In: Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance. Miami, Florida, USA: IEEE, 2003. 143-150 |
[52] |
Hotelling H. Relations between two sets of variates. Biometrika, 1936, 28(3-4):321-377 doi: 10.1093/biomet/28.3-4.321 |
[53] |
Bashir K, Xiang T, Gong S G. Cross-view gait recognition using correlation strength. In: Proceedings of the British Machine Vision Conference. Aberystwyth, UK: BMVA Press, 2010. 109.1-109.11 |
[54] |
Hu H F. Multiview gait recognition based on patch distribution features and uncorrelated multilinear sparse local discriminant canonical correlation analysis. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(4):617-630 doi: 10.1109/TCSVT.2013.2280098 |
[55] |
Xing X L, Wang K J, Yan T, Lv Z W. Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recognition, 2016, 50:107-117 doi: 10.1016/j.patcog.2015.08.011 |
[56] |
Wang K J, Yan T. An improved kernelized discriminative canonical correlation analysis and its application to gait recognition. In: Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China: IEEE, 2012. 4869-4874 |
[57] |
Luo C, Xu W J, Zhu C Y. Robust gait recognition based on partitioning and canonical correlation analysis. In: Proceedings of the 2015 IEEE International Conference on Imaging Systems and Techniques. Macau, China: IEEE, 2015. 1-5 |
[58] |
Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y. Gait recognition using a view transformation model in the frequency domain. In: Proceedings of European Conference on Computer Vision. Graz, Austria: Springer-Verlag, 2006. 151-163 |
[59] |
Kusakunniran W, Wu Q, Li H D, Zhang J. Multiple views gait recognition using view transformation model based on optimized gait energy image. In: Proceedings of the 12th International Conference on Computer Vision Workshops. Kyoto, Japan: IEEE, 2009. 1058-1064 |
[60] |
Kusakunniran W, Wu Q, Zhang J, Li H D. Gait recognition under various viewing angles based on correlated motion regression. IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22(6):966-980 doi: 10.1109/TCSVT.2012.2186744 |
[61] |
Zheng S, Zhang J G, Huang K Q, He R, Tan T. Robust view transformation model for gait recognition. In: Proceedings of the 18th International Conference on Image Processing. Brussels, Belgium: IEEE, 2011. 2073-2076 |
[62] |
Hu M D, Wang Y H, Zhang Z X. Cross-view gait recognition with short probe sequences:from view transformation model to view-independent stance-independent identity vector. International Journal of Pattern Recognition and Artificial Intelligence, 2013, 27(6):1350017 doi: 10.1142/S0218001413500171 |
[63] |
Muramatsu D, Shiraishi A, Makihara Y, Uddin M Z, Yagi Y. Gait-based person recognition using arbitrary view transformation model. IEEE Transactions on Image Processing, 2015, 24(1):140-54 doi: 10.1109/TIP.2014.2371335 |
[64] |
Muramatsu D, Makihara Y, Yagi Y. Cross-view gait recognition by fusion of multiple transformation consistency measures. IET Biometrics, 2015, 4(2):62-73 doi: 10.1049/iet-bmt.2014.0042 |
[65] |
Muramatsu D, Makihara Y, Yagi Y. View transformation model incorporating quality measures for cross-view gait recognition. IEEE Transactions on Cybernetics, 2016, 46(7):1602-1615 doi: 10.1109/TCYB.2015.2452577 |
[66] |
Choudhury S D, Tjahjadi T. Robust view-invariant multiscale gait recognition. Pattern Recognition, 2015, 48(3):798-811 doi: 10.1016/j.patcog.2014.09.022 |
[67] |
Liu N, Tan Y P. View invariant gait recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. Texas USA: IEEE, 2010. 1410-1413 |
[68] |
Liu N N, Lu J W, Tan Y P. Joint subspace learning for view-invariant gait recognition. IEEE Signal Processing Letters, 2011, 18(7):431-434 doi: 10.1109/LSP.2011.2157143 |
[69] |
Shawe-Taylor J, Cristianini N. Properties of kernels. Kernel Methods for Pattern Analysis. Cambridge:Cambridge University Press, 2004. 47-84 |
[70] |
Alzate C, Suykens J A K. Multiway spectral clustering with out-of-sample extensions through weighted kernel PCA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(2):335-347 doi: 10.1109/TPAMI.2008.292 |
[71] |
Yang J, Frangi A F, Yang J Y, Zhang D, Jin Z. KPCA Plus LDA:a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2):230-244 doi: 10.1109/TPAMI.2005.33 |
[72] |
Connie T, Goh K O M, Teoh A B J. Multi-view gait recognition using a doubly-kernel approach on the Grassmann manifold. Neurocomputing, 2016, 216:534-542 doi: 10.1016/j.neucom.2016.08.002 |
[73] |
Xu W J, Luo C, Ji A M, Zhu C Y. Coupled locality preserving projections for cross-view gait recognition. Neurocomputing, 2017, 224:37-44 doi: 10.1016/j.neucom.2016.10.054 |
[74] |
Ben X Y, Meng W X, Yan R, Wang K J. An improved biometrics technique based on metric learning approach. Neurocomputing, 2012, 97(1):44-51 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4492014b0866d51f7d2415c3699c60d8 |
[75] |
Ben X Y, Meng W X, Yan R, Wang K J. Kernel coupled distance metric learning for gait recognition and face recognition. Neurocomputing, 2013, 120:577-589 doi: 10.1016/j.neucom.2013.04.012 |
[76] |
Wang K J, Xing X L, Yan T, Lv Z W. Couple metric learning based on separable criteria with its application in cross-view gait recognition. In: Proceedings of the Biometric Recognition. Lecture Notes in Computer Science, vol. 8833. Shenyang, China: Springer, 2014. 347-356 |
[77] |
Al-Tayyan A, Assaleh K, Shanableh T. Decision-level fusion for single-view gait recognition with various carrying and clothing conditions. Image and Vision Computing, 2017, 61:54-69 doi: 10.1016/j.imavis.2017.02.004 |
[78] |
陈伟宏, 安吉尧, 李仁发, 李万里.深度学习认知计算综述.自动化学报, 2017, 43(11):1886-1897 http://www.aas.net.cn/CN/abstract/abstract19164.shtml
Chen Wei-Hong, An Ji-Yao, Li Ren-Fa, Li Wan-Li. Review on deep-learning-based cognitive computing. Acta Automatica Sinica, 2017, 43(11):1886-1897 http://www.aas.net.cn/CN/abstract/abstract19164.shtml |
[79] |
LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. In: Proceedings of the 2010 IEEE International Symposium on Circuits and Systems. Paris, France: IEEE, 2010. 253-256 |
[80] |
Yan C, Zhang B L, Coenen F. Multi-attributes gait identification by convolutional neural networks. In: Proceedings of the 8th International Congress on Image and Signal Processing. Shenyang, China: IEEE, 2015. 642-647 |
[81] |
Wu Z F, Huang Y Z, Wang L. Learning representative deep features for image set analysis. IEEE Transactions on Multimedia, 2015, 17(11):1960-1968 doi: 10.1109/TMM.2015.2477681 |
[82] |
Zhang C, Liu W, Ma H D, Fu H Y. Siamese neural network based gait recognition for human identification. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai, China: IEEE, 2016. 2832-2836 |
[83] |
Tan T N, Wang L, Huang Y Z, Wu Z F. A Gait Recognition Method Based on Depth Learning, CN Patent 201410587758, June 2017 |
[84] |
Wu Z F, Huang Y Z, Wang L, Wang X G, Tan T N. A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(2):209-226 doi: 10.1109/TPAMI.2016.2545669 |
[85] |
Wolf T, Babaee M, Rigoll G. Multi-view gait recognition using 3D convolutional neural networks. In: Proceedings of the 2016 IEEE International Conference on Image Processing. Arizona USA: IEEE, 2016. 4165-4169 |
[86] |
Li C, Min X, Sun S Q, Lin W Q, Tang Z C. DeepGait:a learning deep convolutional representation for view-invariant gait recognition using joint Bayesian. Applied Sciences, 2017, 7(3):210 doi: 10.3390/app7030210 |
[87] |
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 2015 International Conference on Pattern Recognition. California, USA: ICPR, 2015. 212-218 |
[88] |
Yu S Q, Chen H F, Wang Q, Shen L L, Huang Y Z. Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing, 2017, 239:81-93 doi: 10.1016/j.neucom.2017.02.006 |
[89] |
Gers F A, Schraudolph N N, Schmidhuber J. Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 2002, 3:115-143 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=89ea080d0ed753eb233a54138e933da4 |
[90] |
王坤峰, 苟超, 段艳杰, 林懿伦, 郑心湖, 王飞跃.生成式对抗网络GAN的研究进展与展望.自动化学报, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml
Wang Kun-Feng, Gou Chao, Duan Yan-Jie, Lin Yi-Lun, Zheng Xin-Hu, Wang Fei-Yue. Generative adversarial networks:The state of the art and beyond. Acta Automatica Sinica, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml |
[91] |
Cao Z, Simon T, Wei S E, Sheikh Y. Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Hawaii, USA: CCPR, 2017. 1302-1310 |