Journal of Intelligent Systems (May 2021)

Deep Large Margin Nearest Neighbor for Gait Recognition

  • Xu Wanjiang

DOI
https://doi.org/10.1515/jisys-2020-0077
Journal volume & issue
Vol. 30, no. 1
pp. 604 – 619

Abstract

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Gait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolutional neural network to project gait feature onto a metric subspace, under which intra-class gait samples are pulled together as small as possible while inter-class samples are pushed apart by a large margin. We provide an extensive evaluation in terms of various scenarios, namely, normal, carrying, clothing, and cross-view condition on two widely used gait datasets. Experimental results demonstrate that the proposed DLMNN achieves competitive gait recognition performances and promising computational efficiency.

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