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The perspectives in gait recognition

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we provide a detailed information on classical and recent research results in gait recognition. We provide classification of leading concepts, representations, experiments and available datasets. The most promising algorithms are provided with more details and in the end we provide some predictions on future research. Paper contains also summary on methods used in a variety of papers on gait recognition published after 2002.
Rocznik
Strony
73--98
Opis fizyczny
Bibliogr. 59 poz., il., fot. kolor., 1 rys.
Twórcy
  • Lodz University of Technology, Institute of Mathematics, Lodz 90-924, Wolczanska 215, POLAND
Bibliografia
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  • [2] Tao, D., Li, X., Wu, X., and Maybank, S. J., General tensor discriminant analysis and gabor features for gait recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 10, 2007, pp. 1700- 1715.
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  • [6] Liu, Z., Malave, L., Osuntogun, A., Sudhakar, P., and Sarkar, S., Toward understanding the limits of gait recognition, In: Defense and Security, International Society for Optics and Photonics, 2004, pp. 195-205.
  • [7] Xu, D., Yan, S., Tao, D., Zhang, L., Li, X., and Zhang, H.-J., Human gait recognition with matrix representation, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 16, No. 7, 2006, pp. 896-903.
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  • [26] Chen, X., Weng, J., Lu, W., and Xu, J., Multi-Gait Recognition Based on Attribute Discovery, IEEE transactions on pattern analysis and machine intelligence, Vol. 40, No. 7, 2018, pp. 1697-1710.
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  • [28] Zhang, Z., Chen, J., Wu, Q., and Shao, L., GII Representation-Based Cross- View Gait Recognition by Discriminative Projection With List-Wise Constraints, IEEE transactions on cybernetics, 2017.
  • [29] Hongye, X. and Zhuoya, H., Gait recognition based on gait energy image and linear discriminant analysis, In: Signal Processing, Communications and Computing (ICSPCC), 2015 IEEE International Conference on, IEEE, 2015, pp. 1-4.
  • [30] Fan, Z., Jiang, J., Weng, S., He, Z., and Liu, Z., Human Gait Recognition Based on Discrete Cosine Transform and Linear Discriminant Analysis, In: Signal Processing, Communications and Computing (ICSPCC), 2016 IEEE International Conference on, IEEE, 2016, pp. 1-6.
  • [31] Verlekar, T. T., Correia, P. L., and Soares, L. D., View-invariant gait recognition system using a gait energy image decomposition method, IET Biometrics, Vol. 6, No. 4, 2017, pp. 299-306.
  • [32] Wang, H., Fan, Y., Fang, B., and Dai, S., Generalized linear discriminant analysis based on euclidean norm for gait recognition, International Journal of Machine Learning and Cybernetics, pp. 1-8.
  • [33] Xing, X., Wang, K., Yan, T., and Lv, Z., Complete canonical correlation analysis with application to multi-view gait recognition, Pattern Recognition, Vol. 50, 2016, pp. 107-117.
  • [34] Abdullah, B. A. and El-Alfy, E.-S. M., Statistical Gabor-Based Gait Recognition Using Region-Level Analysis, In: Modelling Symposium (EMS), 2015 IEEE European, IEEE, 2015, pp. 137-141.
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  • [37] Zhang, C., Liu, W., Ma, H., and Fu, H., Siamese neural network based gait recognition for human identification, In: Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, IEEE, 2016, pp. 2832-2836.
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  • [41] Liu, Z. and Sarkar, S., Outdoor recognition at a distance by fusing gait and face, Image and Vision Computing, Vol. 25, No. 6, 2007, pp. 817-832.
  • [42] Hong, L., Jain, A. K., and Pankanti, S., Can multibiometrics improve performance? In: Proceedings AutoID, Vol. 99, Citeseer, 1999, pp. 59-64.
  • [43] Liu, H., Cao, Y., and Wang, Z., A novel algorithm of gait recognition, In: Wireless Communications Signal Processing, 2009. WCSP 2009. International Conference on, Nov 2009, pp. 1-5.
  • [44] Wang, Y., Yu, S., Wang, Y., and Tan, T., Gait recognition based on fusion of multi-view gait sequences, In: International Conference on Biometrics, Springer, 2006, pp. 605-611.
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  • [49] Makihara, Y., Mannami, H., Tsuji, A., Hossain, M. A., Sugiura, K., Mori, A., and Yagi, Y., The OU-ISIR gait database comprising the treadmill dataset, IPSJ Transactions on Computer Vision and Applications, Vol. 4, 2012, pp. 53-62.
  • [50] Liu, Z., Malave, L., and Sarkar, S., Studies on silhouette quality and gait recognition, In: Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, Vol. 2, IEEE, 2004, pp. II-II.
  • [51] Su, H. and Huang, F.-G., Human gait recognition based on motion analysis, In: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, Vol. 7, IEEE, 2005, pp. 4464-4468.
  • [52] Ye, B. andWen, Y.-M., Gait recognition based on DWT and SVM, In:Wavelet Analysis and Pattern Recognition, 2007. ICWAPR’07. International Conference on, Vol. 3, IEEE, 2007, pp. 1382-1387.
  • [53] Arai, K. and Andrie, R., Human gait gender classification using 2D discrete wavelet transforms energy, IJCSNS International Journal of Computer Science and Network Security, Vol. 2, No. 12, 2011, pp. 62-68.
  • [54] Yaacob, N. I., Tahir, N. M., and Abdullah, R., Gait recognition based on lower limb, In: Control and System Graduate Research Colloquium (ICSGRC), 2012 IEEE, IEEE, 2012, pp. 294-297.
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  • [57] Che, L. and Kong, Y., Gait recognition based on DWT and t-SNE, 2015.
  • [58] Yang, H.-D. and Lee, S.-W., Reconstruction of 3D human body pose for gait recognition, In: International Conference on Biometrics, Springer, 2006, pp. 619-625.
  • [59] Alaqtash, M., Sarkodie-Gyan, T., Yu, H., Fuentes, O., Brower, R., and Abdelgawad, A., Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms, In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, IEEE, 2011, pp. 453-457.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2a97de5c-df13-4d47-a520-441cfda70a3b
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