Bimodal Person Re-identification in Multi-camera System

  • Hazar MlikiEmail author
  • Mariem Naffeti
  • Emna Fendri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10617)


This paper introduces a new method to enhance person re-identification by combining person appearance and face modalities in a multi-camera system. The use of face modality requires a preprocessing step of face pose estimation. Therefore, we proposed a new method for face pose estimation in low-resolution context. As for the extraction of person appearance signature, it was performed on discriminant stripes selected automatically. We evaluated the proposed pose estimation method as well as the process of re-identification based on appearance and face modalities on the challenging VIPeR database. The experimental results show that the combination of person appearance and face modalities leads to promising results.


Video surveillance Person re-identification Face pose estimation HOG LBP SVM 


  1. 1.
    Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: BMVC, vol. 2, no. 5, p. 6 (2011)Google Scholar
  2. 2.
    Derbel, A., Jemaa, Y.B., Canals, R., Emile, B., Treuillet, S., Hamadou, A.B.: Comparative study between color texture and shape descriptors for multi-camera pedestrians identification. In: International Conference on Image Processing Theory, Tools and Applications, pp. 313–318 (2012)Google Scholar
  3. 3.
    Bialkowski, A., Denman, S., Sridharan, S., Fookes, C., Lucey, P.: A database for person re-identification in multi camera surveillance networks. In: International Conference on Digital Image Computing Techniques and Applications (2012)Google Scholar
  4. 4.
    Yang, Z., Jin, L., Tao, D.: A comparative study of several feature extraction methods for person re-identification. In: Zheng, W.-S., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds.) CCBR 2012. LNCS, vol. 7701, pp. 268–277. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  5. 5.
    Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2360–2367 (2010)Google Scholar
  6. 6.
    Yuan, L., Tian, Z.: Person re-identification based on color and texture feature fusion. In: Huang, D.-S., Jo, K.-H. (eds.) ICIC 2016. LNCS, vol. 9772, pp. 341–352. Springer, Cham (2016). CrossRefGoogle Scholar
  7. 7.
    Nambiar, A., Nascimento, J.C., Bernardino, A., Santos-Victor, J.: Person re-identification in frontal gait sequences via histogram of optic flow energy image. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2016. LNCS, vol. 10016, pp. 250–262. Springer, Cham (2016). CrossRefGoogle Scholar
  8. 8.
    Bedagkar-Gala, A., Shah, S.K.: Part-based spatio-temporal model for multi-person re-identification. Pattern Recogn. Lett. 33, 1908–1915 (2012)CrossRefGoogle Scholar
  9. 9.
    Liu, Z., Zhang, Z., Wu, Q., Wang, Y.: Enhancing person re-identification by integrating gait biometric. Neurocomputing 168, 1144–1156 (2015)CrossRefGoogle Scholar
  10. 10.
    Zell, A.: Real time face tracking and pose estimation using an adaptive correlation filter for human-robot interaction. In: Mobile Robots (ECMR), pp. 119–124 (2013)Google Scholar
  11. 11.
    Chen, J., Wu, J., Richter, K., Konrad, J., Ishwar, P.: Estimating head pose orientation using extremely low resolution images. In: IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 65–68 (2016)Google Scholar
  12. 12.
    Grimson, Y., Stauffer, C., Romano, R., Lee, L.: Using adaptive tracking to classify and monitor activities in a site. In: The Computer Vision Pattern Recognition, pp. 22–29 (1998)Google Scholar
  13. 13.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 (2005)Google Scholar
  14. 14.
    Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. In: 12th International Conference on Pattern Recognition (1994)Google Scholar
  15. 15.
    Frikha, M., Fendri, E., Hammami, M.: A new appearance signature for real time person re-identification. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds.) IDEAL 2014. LNCS, vol. 8669, pp. 175–182. Springer, Cham (2014). Google Scholar
  16. 16.
    Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distribution. Bull. Calcutta Math. Soc. 35, 99–109 (1943)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)CrossRefGoogle Scholar
  18. 18.
    Minut, S., Mahadevan, S., Henderson, J.M., Dyer, F.C.: Face recognition using foveal vision. In: IEEE International Workshop on Biologically Motivated Computer Vision, pp. 424–433 (2000)Google Scholar
  19. 19.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  20. 20.
    Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  21. 21.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29, 51–59 (1996)CrossRefGoogle Scholar
  22. 22.
    Pietikäinen, M., Ojala, T., Xu, Z.: Rotation-invariant texture classification using feature distributions. Pattern Recogn. 33, 43–52 (2000)CrossRefGoogle Scholar
  23. 23.
    Rahim, M.A., Azam, M.S., Hossain, N., Islam, M.R.: Face recognition using local binary patterns (LBP). Glob. J. Comput. Sci. Technol. 13 (2013)Google Scholar
  24. 24.
    Kwong, J.N.S., Gong, S.: Learning support vector machines for a multi-view face model. In: Proceedings of the British Machine Vision Conference (1999)Google Scholar
  25. 25.
    Li, Y., Gong, S., Sherrah, S., Liddell, H.: Support vector machine based multiview face detection and recognition. J. Image Vis. Comput. 22, 413–427 (2014)CrossRefGoogle Scholar
  26. 26.
    Patacchiola, M., Cangelosi, A.: Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods. J. Pattern Recogn. 71, 132–143 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.MIRACL-ENET’COM, University of SfaxSfaxTunisia
  2. 2.MIRACL-FSS, University of SfaxSfaxTunisia

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