Cross-view gait recognition based on gait sequence

doi:10.3969/j.issn.1000-1565.2021.03.014

Abstract

Abstract: In order to improve the accuracy of cross-view gait recognition and fully extract the temporal information in gait, we proposed a cross-view gait recognition model based on gait image sequence. The model combines encoder and triplet-loss to extract the features from gait sequences. Meanwhile, using a generator and discriminators, molel optimizes the encoder according the discriminant loss so that extracts the temporal information in gait. In order to validate the effectiveness of the proposed method, we conducted experimental studies on CASIA-B and OU-MVLP datasets. Moreover, we also compare with other state-of-arts methods which are based on convolutional neural network and gait energy image.

Key words: convolutional neural network, generator, discriminator, feature extraction, gait recognition

CLC Number:

TH213.3

LI Kai, CAO Kefan, SHEN Haoning. Cross-view gait recognition based on gait sequence[J]. Journal of Hebei University(Natural Science Edition), 2021, 41(3): 311-320.

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