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Human gait recognition based on deterministic learning through multiple views fusion.pdf
Pattern Recognition Letters 78 (2016) 56–63
Contents lists available at ScienceDirect
Pattern Recognition Letters
journal homepage: /locate/patrec
Human gait recognition based on deterministic learning through multiple views fusion?
Muqing Deng a,b, Cong Wang a,b,?, Qingfeng Chen a,b
a College of Automation, South China University of Technology, Guangzhou 510640, Guangdong, China b Key Laboratory of Biomedical Engineering in Guangdong, Guangzhou 510640, Guangdong, China
article info
Article history: Received 11 December 2015 Available online 27 April 2016
Keywords: Gait recognition Deterministic learning Multiple views fusion Gait variability
abstract
Gait characteristics extracted from one single camera are limited and not comprehensive enough to develop a robust recognition system. This paper proposes a robust gait recognition method using multiple views fusion and deterministic learning. First, a multiple-views fusion strategy is introduced, in which gaits collected under different views are synthesized as a kind of synthesized silhouette images. Second, the synthesized silhouettes are characterized with four kinds of time-varying gait features, including three width features of the silhouette and one silhouette area feature. Third, gait variability underlying different individuals’ time-varying gait features is effectively modeled by using deterministic learning algorithm. This kind of variability re?ects the change of synthesized silhouettes while preserving temporal dynamics information of human walking. Gait patterns are represented as the gait variability underlying time-varying gait features and a rapid recognition scheme is presented in published gait databases. Experimental results show that encouraging recognition accuracy can be achieved.
? 2016 Elsevier B.V. All rights reserved.
1. Introduction
Gait is an attractive biometric feature for human identi?cation at a distance. Compared with other biometrics, gait has great prominent advantages of being non-
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