One-Shot Person Re-identification with a Consumer Depth Camera

  • Matteo MunaroEmail author
  • Andrea Fossati
  • Alberto Basso
  • Emanuele Menegatti
  • Luc Van Gool
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


In this chapter, we propose a comparison between two techniques for one-shot person re-identification from soft biometric cues. One is based upon a descriptor composed of features provided by a skeleton estimation algorithm; the other compares body shapes in terms of whole point clouds. This second approach relies on a novel technique we propose to warp the subject’s point cloud to a standard pose, which allows to disregard the problem of the different poses a person can assume. This technique is also used for composing 3D models which are then used at testing time for matching unseen point clouds. We test the proposed approaches on an existing RGB-D re-identification dataset and on the newly built BIWI RGBD-ID dataset. This dataset provides sequences of RGB, depth, and skeleton data for 50 people in two different scenarios and it has been made publicly available to foster advancement in this new research branch.



The authors would like to thank all the people at the BIWI laboratory of ETH Zurich who took part in the BIWI RGBD-ID dataset.


  1. 1.
    Apostoloff, N., Zisserman, A.: Who Are You? - Real-time Person Identification. In: British Machine Vision Conference (2007)Google Scholar
  2. 2.
    Barbosa, B.I., Cristani, M., Del Bue, A., Bazzani, L., Murino, V.: Re-identification with rgb-d sensors. In: First International Workshop on Re-identification (2012)Google Scholar
  3. 3.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  4. 4.
    Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130–144 (2013)Google Scholar
  5. 5.
    Bedagkar-Gala, A., Shah, S.: Multiple person re-identification using part based spatio-temporal color appearance model. In: Computational Methods for the Innovative Design of Electrical Devices’11, pp. 1721–1728 (2011)Google Scholar
  6. 6.
    Besl, P.J., McKay, N.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)CrossRefGoogle Scholar
  7. 7.
    Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches and challenges in 3d and multi-modal 3d + 2d face recognition. Comput. Vis. Image Underst. 101(1), 1–15 (2006)CrossRefGoogle Scholar
  8. 8.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Three-dimensional face recognition. Int. J. Comput. Vision 64, 5–30 (2005)CrossRefGoogle Scholar
  9. 9.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Topology-invariant similarity of nonrigid shapes. Int. J. Comput. Vision 81, 281–301 (2009)CrossRefGoogle Scholar
  10. 10.
    Brunelli, R., Falavigna, D.: Person identification using multiple cues. IEEE Trans. Pattern Anal. Mach. Intell. 17(10), 955–966 (1995)CrossRefGoogle Scholar
  11. 11.
    Cortes, C., Vapnik, V.N.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)Google Scholar
  12. 12.
    Dantone, M., Gall, J., Fanelli, G., Gool, L.V.: Real-time facial feature detection using conditional regression forests. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)Google Scholar
  13. 13.
    Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: European Conference on Computer Vision, vol. 5302, pp. 262–275 (2008)Google Scholar
  14. 14.
    Hong, L., Jain, A., Pankanti, S.: Can multibiometrics improve performance? In: Proceedings IEEE Workshop on Automatic Identification Advanced Technologies, pp. 59–64 (1999)Google Scholar
  15. 15.
    Jain, A.K., Dass, S.C., Nandakumar, K.: Can soft biometric traits assist user recognition? In: Proceedings of SPIE, Biometric Technology for Human Identification 5404, 561–572 (2004)Google Scholar
  16. 16.
    Lee, S.U., Cho, Y.S., Kee, S.C., Kim, S.R.: Real-time facial feature detection for person identification system. In: Machine Vision and Applications, pp. 148–151 (2000)Google Scholar
  17. 17.
    Leyvand, T., Meekhof, C., Wei, Y.C., Sun, J., Guo, B.: Kinect identity: Technology and experience. Computer 44(4), 94–96 (2011)CrossRefGoogle Scholar
  18. 18.
    Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3d points. In: IEEE International Workshop on CVPR for Human Communicative Behavior Analysis (in conjunction with CVPR 2010), San Francisco (2010)Google Scholar
  19. 19.
    Ober, D., Neugebauer, S., Sallee, P.: Training and feature-reduction techniques for human identification using anthropometry. In: Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–8 (2010)Google Scholar
  20. 20.
    Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: A comprehensive multimodal human action database. In: Proceeding of the IEEE Workshop on Applications on Computer Vision (2013)Google Scholar
  21. 21.
    Preis, J., Kessel, M., Werner, M., Linnhoff-Popien, C.: Gait recognition with kinect. In: Proceedings of the First Workshop on Kinect in Pervasive Computing (2012)Google Scholar
  22. 22.
    Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recogn. Lett. 24, 2115–2125 (2003)CrossRefGoogle Scholar
  23. 23.
    Satta, R., Pala, F., Fumera, G., Roli, F.: Real-time appearance-based person re-identification over multiple Kinect cameras. In: VisApp (2013)Google Scholar
  24. 24.
    Shotton, J., Fitzgibbon, A.W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1297–1304 (2011)Google Scholar
  25. 25.
    Sung, J., Ponce, C., Selman, B., Saxena, A.: Unstructured human activity detection from rgbd images. In: International Conference on Robotics and Automation (2012)Google Scholar
  26. 26.
    Velardo, C., Dugelay, J.L.: Improving identification by pruning: A case study on face recognition and body soft biometric. In: International Workshop on Image and Audio Analysis for Multimedia Interactive Services, pp. 1–4 (2012)Google Scholar
  27. 27.
    Viola, P.A., Jones, M.J.: Robust real-time face detection. In: International Conference on Computer Vision, p. 747 (2001)Google Scholar
  28. 28.
    Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Ma, Y.: Towards a practical face recognition system: Robust registration and illumination by sparse representation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 597–604 (2009)Google Scholar
  29. 29.
    Wang, S., Lewandowski, M., Annesley, J., Orwell, J.: Re-identification of pedestrians with variable occlusion and scale. In: International Conference on Computer Vision Workshops, pp. 1876–1882 (2011)Google Scholar
  30. 30.
    Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)Google Scholar
  31. 31.
    Wang, C., Zhang, J., Pu, J., Yuan, X., Wang, L.: Chrono-gait image: A novel temporal template for gait recognition. In: Proceedings of the 11th European Conference on Computer Vision, pp. 257–270 (2010)Google Scholar
  32. 32.
    Wolf, C., Mille, J., Lombardi, E., Celiktutan, O., Jiu, M., Baccouche, M., Dellandrea, E., Bichot, C.E., Garcia, C., Sankur, B.: The liris human activities dataset and the icpr 2012 human activities recognition and localization competition. Tech. Rep. RR-LIRIS-2012-004 (2012)Google Scholar
  33. 33.
    Zhang, H., Parker, L.E.: 4-dimensional local spatio-temporal features for human activity recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2044–2049 (2011)Google Scholar
  34. 34.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)CrossRefGoogle Scholar
  35. 35.
    Zhu, P., Zhang, L., Hu, Q., Shiu, S.: Multi-scale patch based collaborative representation for face recognition with margin distribution optimization. In: European Conference on Computer Vision, pp. 822–835 (2012)Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Matteo Munaro
    • 1
    Email author
  • Andrea Fossati
    • 2
  • Alberto Basso
    • 1
  • Emanuele Menegatti
    • 1
  • Luc Van Gool
    • 2
  1. 1.Intelligent Autonomous Systems LaboratoryUniversity of PaduaPaduaItaly
  2. 2.Computer Vision LaboratoryETH ZurichZurichSwitzerland

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