Recognition of 3D Faces with Missing Parts Based on SIFT and LBP Methods

Chapter
First Online:
Part of the Signal Processing for Security Technologies book series (SPST)

Abstract

Presently, 3D face recognition researched solutions confronted the problem of recognizing 2D. In our contribution, we specifically discuss major difficulties further to propose and test a novel solution of 3D face recognition that is significantly capable to perform the recognition subject, in cases where the analysis of only a part of the face. With the proposed approach, the distinctive features of the face are captured by first extracting SIFT keypoints on the face of analysis and measure how the face changes along profiles built between pairs of keypoints, second we applied the operator SIFT on LBPP,R images, separately. Following the work of Faltemier and al. then Tang and al., we can better detect a number of keypoints by using SIFT on LBPP, R images, than using SIFT on the original images. The contribution is tested using the whole of the Face Recognition Grand Challenge FRGC v1.0 data. Finally, we perform a classification based on SVM process.

Keywords

3D biometric model Biometrics Face recognition Missing parts FRGC V1.0 database 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, W. Worek. Overview of the face recognition grand challenge, in IEEE Workshop on Face Recognition Grand Challenge Experiments, San Diego, CA, June 2005, pp. 947–954Google Scholar
  2. 2.
    K.W. Bowyer, K.I. Chang, P.J. Flynn, A survey of approaches and challenges in 3d and multi-modal 3D + 2D face recognition, January (2006)Google Scholar
  3. 3.
    S. Berretti, A. Del Bimbo, P. Pala. Recognition of 3D faces with missing parts based on profile networks, in Proceedings of the ACM Workshop on 3D Object Retrieval (3DOR '10), ACM, USA, 2010, pp. 81–86Google Scholar
  4. 4.
    A. Colombo, C. Cusano, R. Schettini, Gappy PCA classification for occlusion tolerant 3D face detection. J. Math. Imaging Vis. 35, 193–207 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    N. Alyuz, B.G. Okberk, L. Akarun, 3d face recognition system for expression and occlusion invariance, in IEEE 2nd International Conference on Biometrics, September 2008Google Scholar
  6. 6.
    A. Savran, N. Alyuz, H. Dibeklioglu, O. Celiktutan, B. Gdkberk, B. Sankur, L. Akarun, Bosphorus database for 3D face analysis, in Proceedings of First COST 2101 Workshop on Biometrics and Identity Management, May 2008Google Scholar
  7. 7.
    K.I. Chang, K.W. Bowyer, P.J. Flynn, Multiple nose region matching for 3d face recognition under varying facial expression. IEEE Trans. Pattern Anal. Mach. Intell. 28(6), 1695–1700 (2006)CrossRefGoogle Scholar
  8. 8.
    S. Berretti, A. Del Bimbo, P. Pala, 3d face recognition using iso-geodesic stripes, in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010Google Scholar
  9. 9.
    S. Berretti, A. Del Bimbo, P. Pala, Facial curves between keypoints for recognition of 3D faces with missing parts, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 20–25 June 2011, pp. 46–51Google Scholar
  10. 10.
    A.S. Mian, M. Bennamoun, R. Owens, Keypoint detection and local feature matching for textured 3d face recognition. Int. J. Comput. Vision 79(1), 1–12 (2008)Google Scholar
  11. 11.
    D. Lowe, Distinctive image features from scale-invariant key points. International Journal of Computer Vision, 60(2), 91–110, (2004)Google Scholar
  12. 12.
    M. Mayo, E. Zhang, 3d face recognition using multiview key point matching, in IEEE International Conference on Advanced Video and Signal Based Surveillance, Genoa, Italy, September 2009, pp. 290–295Google Scholar
  13. 13.
    R. Ohbuchi, T. Furuya, Scale-weighted dense bag of visual features for 3d model retrieval from a partial view 3d model, in Proceedings of Workshop on Search in 3D and Video, Kyoto, Japan, September 2009Google Scholar
  14. 14.
    S. Berretti, A. Del Bimbo, P. Pala, B. Ben Amor, M. Daoudi, Selected sift features for 3d facial expression recognition, Turkey, August 2010Google Scholar
  15. 15.
    W. Zheng, H. Tang, Z. Lin, T.S. Huang, A novel approach to expression recognition from non-frontal face images, in Proceedings of IEEE International Conference on Computer Vision, Kyoto, Japan, September 2009, pp. 1901–1908Google Scholar
  16. 16.
    H. Tang, B. Yin, Y. Sun, Y. Hu, 3D face recognition using local binary patterns. Signal Process. 93, 2190–2198 (2013)CrossRefGoogle Scholar
  17. 17.
    M. Pietikinen, A. Hadid, G. Zhao, T. Ahonen, Computer vision using local binary patterns. Comput. Imaging Vision 40 (2011)Google Scholar
  18. 18.
    S. Brahnam, L.C. Jain, L. Nanni, A. Lumini, Local Binary Patterns: New Variants and Applications, Studies in Computational Intelligence, vol 506 (Springer, 2014)Google Scholar
  19. 19.
    T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefMATHGoogle Scholar
  20. 20.
    X. Tan, B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19 (2010)Google Scholar
  21. 21.
    P.J. Besl, N.D. McKay, A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)Google Scholar
  22. 22.
    A. Atia, Face Recognition in Computer Biometric Authentication, for Academic Master in Computer Science (Biskra University, 2011)Google Scholar
  23. 23.
    L. Allano, Scores of merger strategies and dependence measures applied to the bases of virtual people. Thesis of Doctor of the National Institute of Telecommunications, January 12, 2009Google Scholar
  24. 24.
    R.M. Bolle, J.H. Connell, S. Pankanti, A.W. Senior, The relation between the ROC curve and the CMC. IBM Research Report (2006)Google Scholar
  25. 25.
    D. Lowe, Demo Software: SIFT Keypoint Detector, http://www.cs.ubc.edu.ca/lowe/ (2006)
  26. 26.
    W. Hwang, H. Wang, H. Kim, S. Kee, S.-C. Kee, J. Kim, Face recognition system using multiple face model of hybrid fourier. IEEE Trans. Image Process. 20(4), 1152–1165 (2011)Google Scholar
  27. 27.
    V.N. Vapnik, The Nature of Statistical Learning Theory (1995)Google Scholar
  28. 28.
    F. Wang, J. Han, Multimodal biometric authentication based on score level fusion using support vector machine. Opto-Electron. 17(1), (2009)Google Scholar
  29. 29.
    D.A. Ramli, S.A. Samad, A. Hussain, Score information decision fusion using support vector machine for a correlation filter based speaker authentication system, in Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS’08, vol. 53, pp. 235–242 (2009)Google Scholar
  30. 30.
    P. Ejarque, J. Hernado, D. Hernando, D. Gómez, Eigenfeatures and supervectors in feature and score fusion for SVM face and speaker verification. Biomet. ID Manage. Multimodal Commun. 5707, 81–88 (2009)CrossRefGoogle Scholar
  31. 31.
    D.R. Kisku, P. Gupta, J.K. Sing, Fusion of multiple matchers using SVM for offline signature identification. Secur. Technol. 58, 201–208 (2009)CrossRefMATHGoogle Scholar
  32. 32.
    M. Farrús, P. Ejarque, A. Temko, J. Hernando, Histogram equalization in SVM multimodal person verification. Adv. Biomet. 4642, 819–827 (2007)CrossRefGoogle Scholar
  33. 33.
    A. Ouamene, Biometric recognition multimodal fusion of 2D and 3D Face, for Doctor of Science in Electronics, June (2015)Google Scholar
  34. 34.
  35. 35.
    D. Harwood, T. Ojala, M. Pietikäinen, S. Kelman, S. Davis, Texture Classification by Center-Symmetric Auto-correlation, Using Kullback Discrimination of Distributions, Computer Vision Laboratory University of Maryland, College Park, Maryland (1993)Google Scholar
  36. 36.
    T.C. Faltemier, K.W. Bowyer, P.J. Flynn, A region ensemble for 3d face recognition. IEEE Trans. Inf. Forensics Secur. 3(1), 62–73 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.LESIA Laboratory, Department of Computer ScienceUniversity of BiskraBiskraAlgeria

Personalised recommendations