Cross-view Gait Identification based on Convolution Neural Network with Joint Loss Function

Title
Cross-view Gait Identification based on Convolution Neural Network with Joint Loss Function
Authors
박성기김창환이장우
Issue Date
2019-05
Publisher
제어로봇시스템학회논문지
Citation
VOL 25, NO 5-469
Abstract
Most current gait recognition approaches based on convolution neural networks (CNNs) do not learn the discriminative features of separable inter-class differences resulting from cross-view data. To improve this discriminative ability, this paper proposes a network that reduces intra-class variation using a center loss function for view-invariant gait recognition. The proposed method achieved 92% accuracy using OU-MVLP, the largest existing gait recognition dataset. Furthermore, a network trained using the OU-MVLP achieved 95% accuracy with the OU-LP . These results demonstrate that the proposed method offers a good generalization performance.
URI
http://pubs.kist.re.kr/handle/201004/70678
ISSN
1976-5622
Appears in Collections:
KIST Publication > Article
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