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Multimedia Tools and Applications

, Volume 77, Issue 10, pp 12331–12347 | Cite as

Combining weighted adaptive CS-LBP and local linear discriminant projection for gait recognition

  • Shanwen Zhang
  • Liqing Zhang
Article
  • 118 Downloads

Abstract

With the increasing demands of the remote surveillance system, the gait based personal identification research has obtained more and more attention from biometric recognition researchers. The gait sequence is easier to be affected by factors than other biometric feathers. In order to achieve better performance of the gait based identification system, in the paper, a local discriminant gait recognition method is proposed by integrating weighted adaptive center symmetric local binary pattern (WACS-LBP) with local linear discriminate projection (LLDP). The proposed method consists of two stages. In the first stage, the robust local weighted histogram feature vector is extracted from each gait image by WACS-LBP. In the second stage, the dimensionality of the extracted feature vector is reduced by LLDP. The highlights of the proposed method are (1) the extracted feature is robust to rotation invariant, and is also tolerant to illumination and pose changes; (2) the low dimensional feature vector reduced by LLDP can preserve the discriminating ability; and (3) the small-sample-size (SSS) problem is avoided naturally. The proposed method is validated and compared with the existing algorithms on a public gait database. The experimental results show that the proposed method is not only effective, but also can be clearly interpreted.

Keywords

Gait recognition Local binary pattern (LBP) Weighted adaptive center symmetric local binary pattern (WACS-LBP) Local linear discriminate projection (LLDP) 

Notes

Acknowledgments

This work is supported by the China National Natural Science Foundation under grant Nos. 61473237. It is also supported by the Shaanxi Natural Science Foundation Research Project under grant No. 2014JM2-6096, Tianjin Research Program of Application Foundation and Advanced Technology No. 14JCYBJC42500 and Tianjin science and technology correspondent project No. 16JCTPJC47300. The authors would like to thank the gait CASIA subset and all the editors and anonymous reviewers for their constructive advices.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Information EngineeringXijing UniversityXi’anChina
  2. 2.Department of Computer Science Virginia TechBlacksburgUSA

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