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This work is focused on person identification task in video sequences. For this task we propose two complementing solutions, which can be applied in different cases: gait and visual recognition. For gait recognition three kinds of features are used: anthropometric features, based on the length of the skeleton segments; relative distance features, based on relative distances between the skeleton joints; and motion features, based on the movement of a joint between two frames. Two versions of the gait recognition algorithm are presented: the first one uses the depth data alongside with the images while the other one uses only the video sequence. For visual recognition from appearance we propose a deep learning algorithm that returns binary image features. Each algorithm was tested on two datasets. Furthermore, we perform experiments on transfer from one dataset to another to check trained model transferability.

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  1. Kuplyakov, D., Shalnov, E., and Konushin, A., Markov chain Monte Carlo based video tracking algorithm, Program. Comput. Software, 2017, vol. 43, no. 4, pp. 224–229.

    Article  MathSciNet  Google Scholar 

  2. Shalnov, E., Gringauz, A., and Konushin, A., Estimation of the people position in the world coordinate system for video surveillance, Program. Comput. Software, 2016, vol. 42, no. 6, pp. 361–366.

    Article  Google Scholar 

  3. Srikrishna Karanam, Mengran Gou, Ziyan Wu, et al., A systematic evaluation and benchmark for person re-identification: Features, metrics, and datasets, 2016, Preprint arXiv:1605.09653.

  4. Man Ju and Bhanu Bir, Individual recognition using gait energy image, IEEE Trans. Pattern Anal. Mach. Intell., 2006, vol. 28, no. 2, pp. 316–322.

  5. Chhatrala Risil and Jadhav Dattatray, V., Gait recognition based on curvelet transform and PCANet, Pattern Recognit. Image Anal., 2017, vol. 27, no. 3, pp. 525–531.

  6. Bobick, A.F. and Johnson, A.Y., Gait recognition using static, activity-specific parameters, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, Hawaii, IEEE, 2001, vol. 1, pp. I-423–I-430.

  7. Lee, L., Grimson, W., and Eric, L., Gait analysis for recognition and classification, IEEE Proc. of the Fifth Conf. Automatic Face and Gesture Recognition, 2002, pp. 155–162.

  8. Dimitris Kastaniotis, Ilias Theodorakopoulos, Christos Theoharatos, et al., A framework for gait-based recognition using Kinect, Pattern Recognit. Lett., 2015, vol. 68, pp. 327–335.

    Article  Google Scholar 

  9. Rouzbeh Sohrab and Babael Mahdi, Human gait recognition using body measures and joint angles, Int. J., 2015, vol. 6, no. 4, pp. 2305–1493.

  10. Yang, K., Dou, Y., Lv, S., et al., Relative distance features for gait recognition with Kinect, J. Visual Commun. Image Representation, 2016, vol. 39, pp. 209–217.

    Article  Google Scholar 

  11. Lin, K., Yang, H.-F., Hsiao, J.-H., et al., Deep learning of binary hash codes for fast image retrieval, in IEEE Proc. of the Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 27–35.

  12. Zheng, L., Yang, Y., and Hauptmann, A.G., Person reidentification: Past, present and future, 2016, Preprint arXiv:1610.02984.

  13. Matsukawa, T., Okabe, T., Suzuki, E., Sato, Y., Hierarchical Gaussian descriptor for person re-identification, in IEEE Proc. of the Conference on Computer Vision and Pattern Recognition, IEEE, 2016, pp. 1363–1372.

  14. Hermans, A., Beyer, L., and Leibe, B., In defense of the triplet loss for person re-identification, 2017, Preprint arXiv:1703.07737.

  15. Zhang, Y., Xiang, T., Hospedales, T.M., et al., Deep Mutual Learning, 2017, Preprint arXiv:1706.00384.

  16. Morozov, F. and Konushin, A., Background subtraction using a convolutional neural network, Proceedings of the 26th International Conference on Computer Graphics and Vision GraphiCon, 2016, pp. 445–447.

  17. Lin, W. and Yang, W., Structured deep hashing with convolutional neural networks for fast person re-identification, 2017, Preprint arXiv:1702.04179.

  18. Breiman, L., Random forests, Mach. Learn., 2001, vol. 45, no. 1, pp. 5–32.

    Article  MATH  Google Scholar 

  19. Halko, N., Martinsson, P.G., and Tropp, J.A., Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions, SIAM Rev., 2011, vol. 53, no. 2, pp. 217–288.

    Article  MathSciNet  MATH  Google Scholar 

  20. Liu, L., Rahimpour, A., Taalimi, A., et al., End-to-end binary representation learning via direct binary embedding, 2017, Preprint arXiv:1703.04960.

  21. Ioffe, S. and Szegedy, C., Batch normalization: Accelerating deep network training by reducing internal covariate shift, in Proceedings of the 32nd International Conference on Machine Learning, 2015, pp. 448–456.

  22. Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition, 2014, Preprint arXiv:1409.1556.

  23. Zheng, L., Shen, L., Tian, L., et al., Scalable person re-identification: A benchmark, in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1116–1124.

  24. Dozat, T., Incorporating Nesterov momentum into adam, 2016.

  25. Hofmann, M., Geiger, J., Bachmann, S., et al., The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits, J. Visual Commun. Image Representation, 2014, vol. 25, no. 1, pp. 195–206.

    Article  Google Scholar 

  26. Andersson, V. and Ara’ujo, R., Person identification using anthropometric and gait data from kinect sensor, in Proceedings of the Twenty-Ninth Association for the Advancement of Artificial Intelligence Conference, AAAI, 2015, pp. 425–431.

  27. Li, W., Zhao, R., Xiao, T., et al., Deepreid: Deep filter pairing neural network for person re-identification, in IEEE Proceedings of the International Conference on Computer Vision and Pattern Recognition, Columbus, 2014, pp. 152–159.

  28. Wikipedia contributors, Information retrieval – Wikipedia, The Free Encyclopedia, 2017. Accessed January 18, 2018. Information_retrieval&oldid=815034293.

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The project was executed with the partial support from the RFBR, grant no. 16-29-09612 OFI-M “Research and development of methods for biometric identification of a person by gait, gestures and constitution in the data of video surveillance”.

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Correspondence to S. Arseev, A. Konushin or V. Liutov.

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1The article was translated by the authors.

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Arseev, S., Konushin, A. & Liutov, V. Human Recognition by Appearance and Gait. Program Comput Soft 44, 258–265 (2018).

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