Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35892
Title: Human gait recognition via sparse discriminant projection learning
Authors: Lai, ZH
Xu, Y
Jin, Z
Zhang, D 
Keywords: Feature extraction
Gait recognition
Linear discriminant analysis (LDA)
Sparse regression
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on circuits and systems for video technology, 2014, v. 24, no. 10, p. 1651-1662 How to cite?
Journal: IEEE transactions on circuits and systems for video technology 
Abstract: As an important biometric feature, human gait has great potential in video-surveillance-based applications. In this paper, we focus on the matrix representation-based human gait recognition and propose a novel discriminant subspace learning method called sparse bilinear discriminant analysis (SBDA). SBDA extends the recently proposed matrix-representation-based discriminant analysis methods to sparse cases. By introducing the L-1 and L-2 norms into the objective function of SBDA, two interrelated sparse discriminant subspaces can be obtained for gait feature extraction. Since the optimization problem has no closed-form solutions, an iterative method is designed to compute the optimal sparse subspace using the L-1 and L-2 norms sparse regression. Theoretical analyses reveal the close relationship between SBDA and previous matrix-representation-based discriminant analysis methods. Since each nonzero element in each subspace is selected from the most important variables/factors, SBDA is potential to perform equivalent to or even better than the state-of-the-art subspace learning methods in gait recognition. Moreover, using the strategy of SBDA plus linear discriminant analysis (LDA), we can further improve the performance. A set of experiments on the standard USF HumanID and CASIA gait databases demonstrate that the proposed SBDA and SBDA + LDA can obtain competitive performance.
URI: http://hdl.handle.net/10397/35892
ISSN: 1051-8215 (print)
1558-2205 (online)
DOI: 10.1109/TCSVT.2014.2305495
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