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 ZhangEmail author
  • Liqing Zhang


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.


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



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.


  1. 1.
    Ahonen T, Hadid A, Pietikäinen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefzbMATHGoogle Scholar
  2. 2.
    Castro FM, Marín-Jiménez MJ, Guil N (2016) Multimodal features fusion for gait, gender and shoes recognition. Mach Vis Appl:1–16Google Scholar
  3. 3.
    Deng M, Wang C, Cheng F et al (2017) Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning. Pattern Recogn 67:186–200CrossRefGoogle Scholar
  4. 4.
    Emdad Hossain SM, Chetty G (2015) Multimodal biometric database for person identification and gait analysis. Intern J Intell Info Process 5(3):1–10Google Scholar
  5. 5.
    Fan Z, Xu Y, Zhang D (2011) Local linear discriminant analysis framework using sample neighbors. IEEE Trans Neural Netw 22(7):1119–1132CrossRefGoogle Scholar
  6. 6.
    Fan Z, Xu Y, Ni M et al (2016) Individualized learning for improving kernel Fisher discriminant analysis. Pattern Recogn 58(C):100–109CrossRefGoogle Scholar
  7. 7.
    Johannson G (1973) Visual perception of biological motion and a model for its analysis. Percept Psychophys 14:201–211CrossRefGoogle Scholar
  8. 8.
    Kim SM, Lee DJ, Chun MG (2014) Infrared gait recognition using wavelet transform and linear discriminant analysis. J Korean Institute Intelligent Sys 24(6):622–627CrossRefGoogle Scholar
  9. 9.
    Lai Z, Xu Y, Jin Z et al (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662CrossRefGoogle Scholar
  10. 10.
    Liu X, Wang H, Wang J et al (2017) Person re-identification by multiple instance metric learning with impostor rejection. Pattern Recogn 67:287–298CrossRefGoogle Scholar
  11. 11.
    Lu J, Wang G, Moulin P (2014) Human identity and gender recognition from gait sequences with arbitrary walking directions. Info Forensics Sec IEEE Trans on 9(1):51–61CrossRefGoogle Scholar
  12. 12.
    Luo J, Zhang J, Zi C et al (2015) Gait recognition using GEI and AFDEI. Int J Optics 2015(5):1–5CrossRefGoogle Scholar
  13. 13.
    H P Mohan Kumar, H S Nagendraswamy (2014) LBP for gait recognition: A symbolic approach based on GEI plus RBL of GEI. International Conference on Electronics and Communication Systems, 1–5.
  14. 14.
    Mohan Kumar HP, Nagendraswamy HS (2014) Symbolic representation and recognition of gait: an approach based on LBP of split gait energy images. Signal Image Process: An Int J (SIPIJ) 5(4):15–28Google Scholar
  15. 15.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Patt Anal Machine Intell 24(7):971–987CrossRefzbMATHGoogle Scholar
  16. 16.
    Pepler PT, Uys DW, Nel DG (2016) Discriminant analysis under the common principal components model. Communications Statistics: Simulation Compt. doi: 10.1080/03610918.2015.1134568
  17. 17.
    Shi Y, Wang XQ, Zhang YC (2015) A recognition method of gait by wavelet transform and genetic algorithm. Appl Mech Mater 701-702:274–278CrossRefGoogle Scholar
  18. 18.
    Sithi Shameem Fathima SMH, Wahida Banu RSD (2015) Human Gait Recognition Using Silhouettes. Int J Appl Eng Res 10:5443–5454Google Scholar
  19. 19.
    Tafazzoli F, Bebis G, Louis S et al (2015) Genetic feature selection for gait recognition. J Electron Imaging 24(1):013036 -1-14CrossRefGoogle Scholar
  20. 20.
    Xing X, Wang K, Lv Z (2015) Fusion of gait and facial features using coupled projections for people identification at a distance. IEEE Signal Process Lett 22(12):2349–2353CrossRefGoogle Scholar
  21. 21.
    Xu Y, Yang J-y, Lu J et al (2004) An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments. Pattern Recogn 37(10):2091–2094CrossRefGoogle Scholar
  22. 22.
    Xue Z, Dong M, Song W et al (2010) Infrared gait recognition based on wavelet transforms and support vector machine. Pattern Recogn 43(8):2904–2910CrossRefzbMATHGoogle Scholar
  23. 23.
    Yang M (2010) Dacheng Tao. Biologically inspired feature manifold for gait recognition Neurocomputing 73:895–902Google Scholar
  24. 24.
    Zhang S, Zhang C (2015) Orthogonal margin maximization projection for gait recognition. Informatica 26(2):357–367MathSciNetCrossRefGoogle Scholar
  25. 25.
    Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928CrossRefGoogle Scholar
  26. 26.
    Zhao Y, Zhang D, Du M (2016) A novel supervised feature extraction algorithm: enhanced within-class linear discriminant analysis. Int J Comput Sci Eng 13(1):13–23CrossRefGoogle Scholar
  27. 27.
    Zheng S, Huang K, Tan T et al (2012) A cascade fusion scheme for gait and cumulative foot pressure image recognition. Pattern Recogn 45(10):3603–3610CrossRefGoogle Scholar

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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