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Multi-view gait recognition using NMF and 2DLDA

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Abstract

View Transformation Model(VTM) is extensively employed in multi-view gait recognition. However, there still exists decline of matching accuracy among view transformation procedures. Particularly, the loss grows rapidly with the increase of the disparity of views. In the face of this difficulty, firstly, Non-negative Matrix Factorization(NMF) is introduced to obtain local structured features of human body to compensate accuracy loss. Moreover, 2D Linear Discriminant Analysis(2DLDA) is applied to improve classification ability by projecting features into a discriminant space. In the end, gait features, the Gait Energy Images(GEIs), is strengthened as 2D Enhanced GEI(2D-EGEI) by using the reconstruction of 2D Principal Component Analysis(2DPCA). Compared with the state-of-the-art, proposed method significantly outperforms the others. Furthermore, the comparisons of two deep learning methods is evaluated as well. Experimental outcomes show that the proposed method provides an alternative way to obtain the approximative outcomes compared with the deep learning methods.

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Acknowledgements

An earlier version of this paper was presented at the 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA) [29]. This work is partially supported by the National Key Research and Development Project of China (2017YFB1301101), and the Natural Science Basic Research Plan in Shaanxi Province of China (Program No.2018JM6104).

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Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

    Chen Wu

  2. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China

    Yonghong Song & Yuanlin Zhang

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  1. Chen Wu
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  2. Yonghong Song
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  3. Yuanlin Zhang
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Correspondence to Yonghong Song.

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Wu, C., Song, Y. & Zhang, Y. Multi-view gait recognition using NMF and 2DLDA. Multimed Tools Appl 78, 35789–35811 (2019). https://doi.org/10.1007/s11042-019-08153-4

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