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How Much Information Kinect Facial Depth Data Can Reveal About Identity, Gender and Ethnicity?

  • Elhocine Boutellaa
  • Messaoud Bengherabi
  • Samy Ait-Aoudia
  • Abdenour HadidEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

Human face images acquired using conventional 2D cameras may have inherent restrictions that hinder the inference of some specific information in the face. The low-cost depth sensors such as Microsoft Kinect introduced in late 2010 allow extracting directly 3D information, together with RGB color images. This provides new opportunities for computer vision and face analysis research. Although more accurate sensors for detailed facial image analysis are expected to be available soon (e.g. Kinect 2), this paper investigates the usefulness of the depth images provided by the current Microsoft Kinect sensors in different face analysis tasks. We conduct an in-depth study comparing the performance of the depth images provided by Microsoft Kinect sensors against RGB counterpart images in three face analysis tasks, namely identity, gender and ethnicity. Four local feature extraction methods are investigated for both face texture and shape description. Moreover, the two modalities (i.e. depth and RGB) are fused to gain insight into their complementarity. The experimental analysis conducted on two publicly available kinect face databases, EurecomKinect and Curtinfaces, yields into interesting results.

Keywords

Face Recognition Local Binary Pattern Depth Image Iterative Close Point Kinect Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition, 2nd edn. Springer, New York (2011)zbMATHGoogle Scholar
  2. 2.
    Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with Microsoft Kinect sensor: A review. IEEE Transactions on Cybernetics 43(5), 1318–1334 (2013)CrossRefGoogle Scholar
  3. 3.
    Andersen, M., Jensen, T., Lisouski, P., Hansen, A., Gregersen, T., Ahrendt, P.: Kinect depth sensor evaluation for computer vision applications. Technical report, Department of Engineering, Aarhus University, Denmark (2012)Google Scholar
  4. 4.
    Fanelli, G., Weise, T., Gall, J., Van Gool, L.: Real time head pose estimation from consumer depth cameras. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 101–110. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  5. 5.
    Huynh, T., Min, R., Dugelay, J.-L.: An efficient LBP-based descriptor for facial depth images applied to gender recognition using RGB-D face data. In: Park, J.-I., Kim, J. (eds.) ACCV Workshops 2012, Part I. LNCS, vol. 7728, pp. 133–145. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  6. 6.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  7. 7.
    Ahonen, T., Rahtu, E., Ojansivu, V., Heikkila, J.: Recognition of blurred faces using local phase quantization. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4, December 2008Google Scholar
  8. 8.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893, June 2005Google Scholar
  9. 9.
    Kannala, J., Rahtu, E.: BSIF: Binarized statistical image features. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1363–1366 (2012)Google Scholar
  10. 10.
    Li, B., Mian, A., Liu, W., Krishna, A.: Using Kinect for face recognition under varying poses, expressions, illumination and disguise. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 186–192, January 2013Google Scholar
  11. 11.
    Goswami, G., Bharadwaj, S., Vatsa, M., Singh, R.: On RGB-D face recognition using kinect. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6, September 2013Google Scholar
  12. 12.
    Min, R., Choi, J., Medioni, G., Dugelay, J.: Real-time 3D face identification from a depth camera. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1739–1742, November 2012Google Scholar
  13. 13.
    Pamplona Segundo, M., Sarkar, S., Goldgof, D., Silva, L., Bellon, O.: Continuous 3D face authentication using RGB-D cameras. In: IEEE Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 64–69 (2013)Google Scholar
  14. 14.
    Huang, Y., Wang, Y., Tan, T.: Combining statistics of geometrical and correlative features for 3D face recognition. In: Proceedings of the British Machine Vision Conference, pp. 879–888, September 2006Google Scholar
  15. 15.
    Mian, A., Bennamoun, M., Owens, R.: An efficient multimodal 2D–3D hybrid approach to automatic face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(11), 1927–1943 (2007)CrossRefGoogle Scholar
  16. 16.
    Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  17. 17.
    Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 947–954 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Elhocine Boutellaa
    • 1
    • 2
  • Messaoud Bengherabi
    • 1
  • Samy Ait-Aoudia
    • 2
  • Abdenour Hadid
    • 3
    Email author
  1. 1.Centre de Développement des Technologies AvancéesBaba HassenAlgeria
  2. 2.Ecole Nationale Supèrieure d’InformatiqueEl HarrachAlgeria
  3. 3.University of OuluOuluFinland

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