Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Face Recognition Using Collaborative Image Set

Author : Sandeep G.S 1 Kotreshi S.N 2 Maruthi S.T 3 Arun Kumar B.T 4

Date of Publication :7th May 2015

Abstract: Digital imaging and communication technologies are developing in recent year; the image set based face recognition (ISFR) is becoming increasingly important. The difficulty in ISFR is how effectively and efficiently represents the query image set by using the gallery image sets. The set-to-set distance based methods ignore the relationship between gallery sets, while representing the query set images individually over the gallery sets ignores the correlation between query set images. In this paper, we propose a novel image set based collaborative representation and classification method for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively over all the gallery sets. With the resolved representation coefficients, the distance between the query set and each gallery set can then be calculated for classification. The proposed model naturally and effectively extends the image based collaborative representation to an image set based one, and our extensive experiments on benchmark ISFR databases show the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.

Reference :

    1. O. Yamaguchi, K. Fukui, and K.-i. Maeda, “Face recognition using temporal image sequence,” in Automatic Face and Gesture Recognition, IEEE Conference on. IEEE, 1998, pp. 318–323.
    2. O. Arandjelovic, G. Shakhnarovich, J. Fisher, R. Cipolla, and T. Darrell, “Face recognition with image sets using manifold density divergence,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2005, pp. 581–588.
    3. R. Wang, S. Shan, X. Chen, and W. Gao, “Manifoldmanifold distance with application to face recognition based on image set,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2008, pp.1–8.
    4. H. Cevikalp and B. Triggs, “Face recognition based on image sets,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2010, pp. 2567–2573.
    5. L. Wolf, T. Hassner, and I. Maoz, “Face recognition in unconstrained videos with matched background similarity,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2011, pp. 529–534.
    6. Y. Hu, A. S. Mian, and R. Owens, “Sparse approximated nearest points for image set classification,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2011, pp. 121–128.
    7. T. Kim, J. Kittler, and R. Cipolla, “Discriminative learning and recognition of image set classes using canonical correlations,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, no. 6, pp. 1005–1018, 2007.
    8. R. Wang and X. Chen, “Manifold discriminant analysis,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2009, pp. 429–436.
    9. H. J. Seo and P. Milanfar, “Face verification using the lark representation,” Information Forensics and Security, IEEE Transactions on, vol. 6, no. 4, pp. 1275–1286, 2011.
    10. K.-C. Lee, J. Ho, M.-H. Yang, and D. Kriegman, “Video-based face recognition using probabilistic appearance manifolds,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2003, pp. 313– 320.
    11. A. Hadid and M. Pietikainen, “From still image to video-based face recognition: an experimental analysis,” in Automatic Face and Gesture Recognition, IEEE Conference on. IEEE, 2004, pp. 813–818.
    12. W. Fan and D.-Y. Yeung, “Locally linear models on face appearance manifolds with application to dual-subspace based classification,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2006, pp. 1384– 1390.
    13. R. Wang, H. Guo, L. S. Davis, and Q. Dai, “Covariance discriminative learning: A natural and efficient approach to image set classification,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2012, pp. 2496– 2503.
    14. R. Caseiro, P. Martins, J. F. Henriques, F. S. Leite, and J. Batista, “Rolling riemannian manifolds to solve the multiclass classification problem,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2013, pp. 41–48.
    15. S. Jayasumana, R. Hartley, M. Salzmann, H. Li, and M. Harandi, “Kernel methods on the riemannian manifold of symmetric positive definite matrices,” in Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2013, pp. 73–80.
    16. G. Baudat and F. Anouar, “Generalized discriminant analysis using a kernel approach,” Neural computation, vol. 12, no. 10, pp. 2385–2404, 2000
    17. H. Cevikalp, B. Triggs, H. S. Yavuz, Y. K ¨uc¸¨uk, M. K¨uc¸¨uk, and A. Barkana, “Large margin classifiers based on affine hulls,” Neurocomputing, vol. 73, no. 16, pp. 3160– 3168, 2010
    18. M. Kim, S. Kumar, V. Pavlovic, and H. Rowley, “Face tracking and recognition with visual constraints in realworld videos,” in Computer Vision and Pattern Recognition, IEEE Conference on.IEEE,2008, pp.1–8.
    19. P. Viola and M. Jones, “Robust real-time face detection,” Interational Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2004.
    20. M. Yang, P. Zhu, L. Van Gool, and L. Zhang,“Face recognition based on regularized nearest points between image sets,” in Automatic Face and Gesture Recognition, IEEE Conference on. IEEE, 2013, pp. 1–7
    21. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 31, no. 2, pp. 210–227, 2009.
    22. S. Gao, I. W.-H. Tsang, and L.-T. Chia, “Kernel sparse representation for image classification and face recognition,” in Computer Vision–ECCV 2010. Springer, 2010, pp. 1–14.

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