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A Hybrid Deep Architecture for Face Recognition in Real-Life Scenario

  • A. Sanyal
  • U. BhattacharyaEmail author
  • S. K. Parui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)

Abstract

This article describes our recent study of a real-life face recognition problem using a hybrid architecture consisting of a very deep convolution neural network (CNN) and a support vector machine (SVM). The novel aspects of this study include (i) implementation of a really deep CNN architecture consisting of 11 layers to study the effect of increasing depth on recognition performance by a subsequent SVM, and (ii) verification of the recognition performance of this hybrid classifier trained by samples of a certain standard size on test face images of smaller sizes reminiscent to various real-life scenarios. Results of the present study show that the features computed at various shallow levels of a deep architecture have identical or at least comparable performances and are more robust than the deepest feature computed at the inner most sub-sampling layer. We have also studied a simple strategy of recognizing face images of very small sizes using this hybrid architecture trained by standard size face images and the recognition performance is reported. We obtained simulation results using the cropped images of the standard extended Yale Face Database which show an interesting characteristic of the proposed architecture with respect to face images captured in a very low intensity lighting condition.

Keywords

Convolutional neural network Support vector machine Face recognition 

References

  1. 1.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997)CrossRefGoogle Scholar
  2. 2.
    Bergstra, J., et al.: Theano: deep learning on GPUs with python. In: Big Learn Workshop, NIPS 2011 (2011)Google Scholar
  3. 3.
    Bledsoe, W.W., Chan, H.: Man-machine facial recognition. Technical report PRI 22, Panoramic Res. Inc., Palo Alto, CA (1966)Google Scholar
  4. 4.
    Brunelli, R., Poggio, T.: Face recognition: feature versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1042–1052 (1993)CrossRefGoogle Scholar
  5. 5.
    Colombo, C., Bimbo, A.D., Magistris, S.D.: Human-computer interaction based on eye movement tracking. In: Proceedings of Computer Architectures for Machine Perception (CAMP 1995), pp. 258–263 (1995)Google Scholar
  6. 6.
    Cox, I.J., Ghosn, J., Yianilos, P.N.: Feature based face recognition using mixture-distance. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1996), pp. 209–216 (1996)Google Scholar
  7. 7.
    Ding, X., Fang, C.: Discussions on some problems in face recognition. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 47–56. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-30548-4_7 CrossRefGoogle Scholar
  8. 8.
    Garcia, C., Delakis, M.: Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1408–1423 (2004)CrossRefGoogle Scholar
  9. 9.
    Goldstein, R.J., Harmon, L.D., Lesk, A.B.: Identification of human faces. Proc. IEEE 59, 748–760 (1971)CrossRefGoogle Scholar
  10. 10.
    Graf, H.P., Chen, T., Petajan, E., Cosatto, E.: Locating faces and facial parts. In: International Workshop on Automatic Face- and Gesture-Recognition, pp. 41–46 (1995)Google Scholar
  11. 11.
    Harmon, L., Khan, M., Lasch, R., Ramig, P.: Machine identification of human faces. Pattern Recogn. 13, 97–110 (1981)CrossRefGoogle Scholar
  12. 12.
    Heseltine, T., Pears, N., Austin, J.: Evaluation of image preprocessing techniques for eigenface based face recognition. In: Proceedings of SPIE, vol. 4875, pp. 677–685 (2002)Google Scholar
  13. 13.
    Huang, F.J., LeCun, Y.: Large-scale learning with SVM and convolutional nets for generic object categorization. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 1, pp. 284–291 (2006)Google Scholar
  14. 14.
    Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. Inf. Process. Syst. 5, 41–68 (2009)CrossRefGoogle Scholar
  15. 15.
    Kanade, T.: Picture processing system by computer complex and recognition of human faces. Kyoto University, Japan, Ph.D. thesis (1973)Google Scholar
  16. 16.
    Kaufman, G.J., Breeding, K.J.: Automatic recognition of human faces from profile silhouettes. IEEE Trans. Syst. Man Cybern. 6, 113–120 (1976)CrossRefzbMATHGoogle Scholar
  17. 17.
    Lawrence, S., et al.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)CrossRefGoogle Scholar
  18. 18.
    Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27, 684–698 (2005)CrossRefGoogle Scholar
  19. 19.
    Liposs̆c̆ak, Z., Lonc̆aric̆, S.: A scale-space approach to face recognition from profiles. In: Proceedings of the International Conference on Computer Analysis of Images and Patterns, pp. 243–250 (1999)Google Scholar
  20. 20.
    Messer, K., et al.: Face authentication test on the BANCA database. In: Proceedings of the International Conference on Pattern Recognition, vol. 4, pp. 523–532 (2004)Google Scholar
  21. 21.
    Nixon, M.: Eye spacing measurement for facial recognition. Proc. SPIE 0575, 279–285 (1985)CrossRefGoogle Scholar
  22. 22.
    Pontil, M., Verri, A.: Support vector machines for 3-D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 20, 637–646 (1998)CrossRefGoogle Scholar
  23. 23.
    Reisfeld, D.: Generalized symmetry transforms : attentional mechanisms and face recognition. Tel-Aviv University, Ph.D. thesis (1994)Google Scholar
  24. 24.
    Roeder, N., Li, X.: Experiments in analyzing the accuracy of facial feature detection. In: Vision Interface 1995, pp. 8–16 (1995)Google Scholar
  25. 25.
    Rowley, H., Baluja, S., Kanade, T.: Neural network based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20, 23–38 (1998)CrossRefGoogle Scholar
  26. 26.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A: Opt. Imaging Sci. Vis. 4, 519–524 (1987)CrossRefGoogle Scholar
  27. 27.
    Tan, X., Chen, S., Zhou, Z., Zhang, F.: Face recognition from a single image per person: a survey. Pattern Recogn. 39, 1725–1745 (2006)CrossRefzbMATHGoogle Scholar
  28. 28.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  29. 29.
    Valentin, D., et al.: Connectionist models of face processing: a survey. Pattern Recogn. 27, 1209–1230 (1994)CrossRefGoogle Scholar
  30. 30.
    Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell 19, 775–779 (1997)CrossRefGoogle Scholar
  31. 31.
    Yuille, A.L., Hallinan, P.W., Cohen, D.S.: Feature extraction from faces using deformable templates. Int. J. Comput. Vis. 8(2), 99–111 (1992)CrossRefGoogle Scholar
  32. 32.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of CSEIndian Institute of TechnologyKanpurIndia
  2. 2.CVPR UnitIndian Statistical InstituteKolkataIndia

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