Abstract
Face Recognition is one of the biometrics that can be used to uniquely identify an individual based on the matching performed against known faces. The real world face recognition is very challenging since the face images acquired may vary with illumination, expression and pose. No existing system can claim that they have handled all these issues well. This work particularly focus on addressing the problems of face images taken in challenging environments. A more efficient Face Recognition system based on a combination of Sparse and Dense representation (SDR) along with Local Correlation is proposed. While considering the efficient methods for classification, Sparse Representation (SR) is the best one. Here a Supervised Low Rank (SLR) decomposition of dictionary is used to implement the SDR framework in the initial step. Then we apply Local Correlation to the cases where SDR-SLR method fails to distinguish competing classes properly. Usually due to changes in illumination and pose, variations can be seen to occur in different face parts. Correlation is calculated between the query image and the images of top matches that are obtained from the SDR-SLR method. Since we compute local correlation of relevant points only within a small dictionary, computation time of the proposed method is very less. Challenging benchmark datasets such as AR, Extended Yale and ORL databases are used for testing the proposed method. Experimental analysis shows that performance of the proposed method is better than the state-of-art face recognition approaches and the performance gains are very high.
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References
Haghighat, M., Abdel-Mottaleb, M.: Lower resolution face recognition in surveillance systems using discriminant correlation analysis. In: 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), pp. 912–917 (2017)
Wang, Q., Elbouz, M., Alfalou, A., Brosseau, C.: Designing a composite correlation filter based on iterative optimization of training images for distortion invariant face recognition. Opt. Lasers Eng. 93, 100–108 (2017)
Jiang, X., Lai, J.: Sparse and dense hybrid representation via dictionary decomposition for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(5), 1067–1079 (2015)
De Marsico, M., Nappi, M., Riccio, D., Wechsler, H.: Robust face recognition for uncontrolled pose and illumination changes. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 43(1), 149–163 (2013)
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Jiang, X.D., Joshi, N., Kadir, T., Brady, M.: IEEE Trans. Pattern Anal. Mach. Intell. 31(5) (2009)
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(7), 711–720 (1997)
Moghaddam, B.: Principal manifolds and probabilistic subspaces for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 780–788 (2002)
Jiang, X.D., Mandal, B., Kot, A.: Enhanced maximum likelihood face recognition. Electron. Lett. 42(19) (2006)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Cands, E.J., Li, X., Ma, Y., Wright, J., Candes, E.J.: Robust principal component analysis? J. ACM 58(3), 137 (2009)
De Marsico, M., Nappi, M., Riccio, D., Tortora, G.: NABS: novel approaches for biometric systems. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(4), 481–493 (2011)
Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark estimation in the wild. In: CVPR, pp. 2879–2886 (2012)
Lee, K., Ho, J.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)
Martinez, A.M.: The AR face database. CVC Technical report (1998)
Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of the 1994 IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)
Deng, W., Hu, J., Guo, J.: Extended SRC: undersampled face recognition via intraclass variant dictionary. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1864–1870 (2012)
Chen, C.F., Wei, C.P., Wang, Y.C.F.: Low-rank matrix recovery with structural incoherence for robust face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2618–2625 (2012)
Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 2106–2112 (2010)
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Sahla Habeeba, M.A., Simon, P., Prajith, R. (2018). Robust Face Recognition Using Sparse and Dense Hybrid Representation with Local Correlation. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-10-8660-1_63
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DOI: https://doi.org/10.1007/978-981-10-8660-1_63
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