بهبود شناسایی هویت از طریق راه رفتن با استفاده از الگوریتم ژنتیک

[1] M. S. Nixon, T. Tan, and R. Chellappa, “Human Identification Based on Gait,” Springer US, 2006.

[2] D. Gafurov, “A survey of biometric gait recognition: approaches, security and challenges,” Annual Norwegian Computer Science Conference, 2007.

[3] M. Ju, B. Bhanu, “Individual recognition using gait energy image,” IEEE Transactions Pattern Analysis Machine Intelligence (IEEE T PATTERN ANAL), vol. 28, no. 2, pp. 316–322, 2006.

[4] I. Bouchrika, M. S. Nixon, “Model-based feature extraction for gait analysis and recognition,” International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications. Springer, 2007.

[5] M. Goffredo, I. Bouchrika, J. N. Carter, et al., “Performance analysis for gait in camera networks,” 1st ACM Workshop on Analysis and Retrieval of Events/Actions and Workflows in Video Streams, ser. AREA ’08. New York, NY, USA: ACM, 2008.

[6] R. Zhang, C. Vogler, and D. Metaxas, “Human gait recognition at sagittal plane,” Image and Vision Computing, vol. 25, no. 3, pp. 321 – 330, 2007.

[7] C. Yam, M. S. Nixon, and J. N. Carter, “Automated person recognition by walking and running via model-based approaches,” Pattern Recognition, vol. 37, no. 5, pp. 1057–1072, 2004.

[8] R. T. Collins, R. Gross, and J. Shi, “Silhouette-based human identification from body shape and gait,” 5th IEEE International Conference on Automatic Face and Gesture Recognition, 2002

[9] E. Zhang, Y. Zhao, and W. Xiong, “Active energy image plus 2dlpp for gait recognition,” Signal Processing, vol. 90, no. 7, pp. 2295–2302, 2010.

[10] K. Bashir, X. Tao, G. Shaogang, “Gait recognition without subject cooperation,” Pattern Recognition Letter, vol. 31, no. 13, pp. 2052–2060, 2010.

[11] Sh. Mukherjee, K. Chaudhary, et al, “Gait recognition using segmented motion flow energy image,” International Conference of IEEE in Computing, Communication and Networking Technologies (ICCCNT), 2019.

[12] S. Yu, R. Liao, W. An, H. Chen, E. B. G. Reyes, Y. Huang, and N. Poh, “GaitGanv2: Invariant gait feature extraction using generative adversarial networks,” Pattern Recognition (PATTERN RECOGN), vol. 87, pp. 179–189, 2019.

[13] K. Zhang, W. Luo, L. Ma, W. Liu, and H. Li, “Learning joint gait representation via quintuplet loss minimization,” in Computer Vision and Pattern Recognition (CVPR), pp. 4700-4709, 2019.

[14] Y. Zhang, Y. Huang, L. Wang, and S. Yu, “A comprehensive study on gait biometrics using a joint CNN-based method,” Pattern Recognition (PATTERN RECOGN), vol. 93, pp. 228–236, 2019.

[15] Y. He, J. Zhang, H. Shan, and L. Wang, “Multi-task GANs for viewspecific feature learning in gait recognition,” IEEE Transactions Information Forensics Security, vol. 14, no. 1, pp. 102–113, Jan. 2019.

[16] B. Hu, Y. Guan, Y. Gao, Y. Long, N. Lane, and T. Ploetz, “Robust cross-view gait recognition with evidence: A discriminant gait GAN (DiGGAN) approach,” 2018, arXiv: 1811.10493. [Online]. Available: http://arxiv.org/abs/1811.10493

[17] Y. Huang et al., “Attention-based network for cross-view gait recognition,” in International Conference Neural Information Process. Cham, Switzerland: Springer, 2018. 

[18] S. Li, W. Liu, and H. Ma, “Attentive Spatial–Temporal summary networks for feature learning in irregular gait recognition,” IEEE Transactions Multimedia, vol. 21, no. 9, pp. 2361–2375, Sep. 2019. 

[19] X. Ben, C. Gong, P. Zhang, R. Yan, Q. Wu, and W. Meng, “Coupled bilinear discriminant projection for cross-view gait recognition,” IEEE Transactions Circuits System Video Technology, vol. 30, no. 3, pp. 734–747, Mar. 2020.

[20] W. Zeng, and Z. Wang, “View-invariant gait recognition via deterministic learning,” Neurocomputing, vol. 175, pp. 324–335, 2016.

[21] X. Huang, and N. V. Boulgouris, “Human gait recognition based on multiview gait sequences,” EURASIP Journal on Advances in Signal Processing, vol. 1, pp. 1–8, 2008.

[22] R. Cilla, M. A. Patricio, A. Berlanga, and J. M. Molina, “A probabilistic, discriminative and distributed system for the recognition of human actions from multiple views,” Neurocomputing, vol. 75, no. 1, pp. 78–87, 2012.

[23] N. Liu, J. Lu, G. Yang, and Y. P. Tan, “Robust gait recognition via discriminative set matching,” Journal of Visual Communication and Image Representation, vol. 24, no. 4, pp. 439–447, 2013.

[24] W. Kusakunniran, Q. Wu, J. Zhang, and H. Li, “Support vector regression for multi-view gait recognition based on local motion feature selection,” International Conference of IEEE in Computer Vision and Pattern Recognition (CVPR), 2010

[25] W. Kusakunniran, Q. Wu, J. Zhang, and H. Li, “Gait recognition under various viewing angles based on correlated motion regression,” IEEE transactions on circuits and systems for video technology, vol. 22, no. 6, pp. 966–980, 2012.

[26] X. Zhao, Y. Jiang, T. Stathaki, and H. Zhang, “Gait recognition method for arbitrary straight walking paths using appearance conversion machine,” Neurocomputing, vol. 173, pp. 530, 540, 2015.

[27] D. Muramatsu, Y. Makihara, and Y. Yagi, “View transformation model incorporating quality measures for cross-view gait recognition,” IEEE transactions on cybernetics, vol. 46, no. 7, pp. 1602–1615, 2016.

[28] N. Liu, J. Lu, and Y. P. Tan, “Joint subspace learning for view-invariant gait recognition,” IEEE Signal Processing Letters, vol. 18, no. 7, pp. 431–434, 2011.

[29] Y. Dupuis, X. Savatier, and P. Vasseur, “Feature subset selection applied to model-free gait recognition,” Image and vision computing, vol. 31, no. 8, pp. 580–591, 2013.

[30] T. Connie, M. K. O. Goh, and A. B. J. Teoh, “A grassmannian approach to address view change problem in gait recognition,” IEEE transactions on cybernetics, vol. 47, no. 6, pp. 1395 – 1408, 2016.

[31] S. Jia, L. Wang, and X. Li, “View-invariant gait authentication based on silhouette contours analysis and view estimation,” IEEE/CAA Journal of Automatica Sinica, vol. 2, no. 2, pp. 226–232, 2015.

[32] T. Verlekar, P. Correia, L. Soares, “View-invariant gait recognition exploiting spatio-temporal information and a dissimilarity metric,” International Conference of IEEE in Biometrics Special Interest Group (BIOSIG), 2016.

[33] J. Tang, J. Luo, T. Tjahjadi, and F. Guo, “Robust arbitrary-view gait recognition based on 3d partial similarity matching,” IEEE Transactions on Image Processing, vol. 26, no.1, pp. 7–22, 2017.

[34] D. S. Choudhury, T. Tardi, “Robust view invariant multiscale gait recognition,” Pattern Recognition, vol. 48, no. 3, pp. 798–811, 2015.

[35] I. Rida, X. Jiang, and G. L. Marcialis, “Human body part selection by group lasso of motion for model-free gait recognition,” IEEE Signal Processing Letters, vol. 23, no. 1, pp. 154–158, 2016.

[36] T. Yeoh, S. Zapotecas-Mart´ınez, Y. Akimoto, H. Aguirre, and K. Tanaka, “Genetic algorithm assisted by a svm for feature selection in gait classification,” in Intelligent Signal Processing and Communication Systems (ISPACS), 2014 International Symposium on. IEEE, 2014.

[37] F. Tafazzoli, G. Bebis, S. Louis, and M. Hussain, “Genetic feature selection for gait recognition,” Journal of Electronic Imaging, vol. 24, no. 1, pp. 013 036–013 036, 2015.

[38] T. Verlekar, P. Correia, L. Soares, “View-invariant gait recognition system using a gait energy image decomposition method,” IET Biometrics, vol. 6, no. 4, pp. 299–306, 2017.

[39] S. Baluja, and R. Caruana, “Removing the genetics from the standard genetic algorithm,” International Conference in Machine Learning, 1995.

[40] S. Yu, D. Tan, and T. Tan, “A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition,” 18th International Conference on Pattern Recognition (ICPR’06), 2006.

[41] Y. Guan, C. Li, Y. Hu, “An adaptive system for gait recognition in multiview environments,” 14th ACM Multimedia and Security Workshop, Coventry, UK, 2012.

[42] X. Zhaopeng, L. Wei, Zh. Qin, et al, “Gait recognition based on capsule network,” Journal of Visual Communication and Image Representation, vol. 59, pp. 159-167, 2019.

 [43] W. Kusakunniran, “Attribute-based learning for gait recognition using spatio-temporal interest points,” Image and Vision Computing, vol. 32, no. 1, pp. 1117–1126, 2014.

[44] P. Arora, M. Hanmandlu, and S. Srivastava, “Gait based authentication using gait information image features,” Pattern Recognition Letters, vol. 68, pp. 336–342, 2015.

 [45] P. Yogarajah, P. Chaurasia, J. Condell, and G. Prasad, “Enhancing gait based person identification using joint sparsity model and -norm minimization,” Information Sciences, vol. 308, pp. 3–22, 2015.

[46] Ch. Xin, W. Jian, L. Wei, et al, “Multi-Gait Recognition Based on Attribute Discovery,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7, pp. 1697 – 1710, 2018.

[47] O. L. Ait, B. Larbi, Kh. Emad, et al, “Human gait recognition using GEI-based local multi-scale feature descriptors,” Multimedia Tools Applications, vol. 78, pp. 5715-5730, 2019.

[48] I. Ebenezer, S. Elias, Rajagopalan and K. Easwarakumar “View-invariant gait recognition through genetic template segmentation,” IEEE Signal Processing Letters, vol. 24, no. 8, pp. 1188-1192, 2017.

[49] I. R. Alvarez, G. S. Alvarez, “Cross-View Gait Recognition Based on U-Net,” International Joint Conference on Neural Networks (IJCNN), 2020.

[50] Z. Zhang et al., “Gait recognition via disentangled representation learning,” IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), Jun. 2019, pp. 4710–4719.

[51] H. Chao, Y. He, J. Zhang, and J. Feng, “Gaitset: Regarding gait as a set for cross-view gait recognition,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 2019, pp. 8126–8133.

[52] H. Wu, J. Tian, Y. Fu, et al, “Condition-Aware Comparison Scheme for Gait Recognition,” IEEE Transactions on Image Processing, vol. 30, pp. 2734-2744, 2020.

[53] Z. Zhang, L. Tran, F. Liu, and X. Liu, “On Learning Disentangled Representations for Gait Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 
pp. 1–1, 2020.