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

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Computer Vision, Graphics, and Image Processing (ICVGIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,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.

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References

  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)

    Article  Google Scholar 

  2. Bergstra, J., et al.: Theano: deep learning on GPUs with python. In: Big Learn Workshop, NIPS 2011 (2011)

    Google Scholar 

  3. Bledsoe, W.W., Chan, H.: Man-machine facial recognition. Technical report PRI 22, Panoramic Res. Inc., Palo Alto, CA (1966)

    Google Scholar 

  4. Brunelli, R., Poggio, T.: Face recognition: feature versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1042–1052 (1993)

    Article  Google Scholar 

  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. 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. 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

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  9. Goldstein, R.J., Harmon, L.D., Lesk, A.B.: Identification of human faces. Proc. IEEE 59, 748–760 (1971)

    Article  Google Scholar 

  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. Harmon, L., Khan, M., Lasch, R., Ramig, P.: Machine identification of human faces. Pattern Recogn. 13, 97–110 (1981)

    Article  Google Scholar 

  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. 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. Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. Inf. Process. Syst. 5, 41–68 (2009)

    Article  Google Scholar 

  15. Kanade, T.: Picture processing system by computer complex and recognition of human faces. Kyoto University, Japan, Ph.D. thesis (1973)

    Google Scholar 

  16. Kaufman, G.J., Breeding, K.J.: Automatic recognition of human faces from profile silhouettes. IEEE Trans. Syst. Man Cybern. 6, 113–120 (1976)

    Article  MATH  Google Scholar 

  17. Lawrence, S., et al.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Nixon, M.: Eye spacing measurement for facial recognition. Proc. SPIE 0575, 279–285 (1985)

    Article  Google Scholar 

  22. Pontil, M., Verri, A.: Support vector machines for 3-D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 20, 637–646 (1998)

    Article  Google Scholar 

  23. Reisfeld, D.: Generalized symmetry transforms : attentional mechanisms and face recognition. Tel-Aviv University, Ph.D. thesis (1994)

    Google Scholar 

  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. Rowley, H., Baluja, S., Kanade, T.: Neural network based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20, 23–38 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  28. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)

    Article  Google Scholar 

  29. Valentin, D., et al.: Connectionist models of face processing: a survey. Pattern Recogn. 27, 1209–1230 (1994)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  32. Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of CSE, Indian Institute of Technology, Kanpur, India

    A. Sanyal

  2. CVPR Unit, Indian Statistical Institute, Kolkata, India

    U. Bhattacharya & S. K. Parui

Authors
  1. A. Sanyal
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  2. U. Bhattacharya
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  3. S. K. Parui
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Corresponding author

Correspondence to U. Bhattacharya .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Chittoor, India

    Snehasis Mukherjee

  2. GE Global Research, Bangalore, India

    Suvadip Mukherjee

  3. Indian Statistical Institute, Kolkata, India

    Dipti Prasad Mukherjee

  4. International Institute of Information Technology, Hyderabad, India

    Jayanthi Sivaswamy

  5. Indian Institute of Technology Bombay, Mumbai, India

    Suyash Awate

  6. CEDAR, Buffalo, New York, USA

    Srirangaraj Setlur

  7. International Institute of Information Technology, Hyderabad, India

    Anoop M. Namboodiri

  8. CSIR-CEERI, Pilani, Rajasthan, India

    Santanu Chaudhury

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Sanyal, A., Bhattacharya, U., Parui, S.K. (2017). A Hybrid Deep Architecture for Face Recognition in Real-Life Scenario. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-68124-5_11

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  • Publisher Name: Springer, Cham

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