Extended Set of DCT-TPLBP and DCT-FPLBP for Face Recognition

Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

Abstract:

In this paper, we describe an application for face recognition. Many studies have used local descriptors to characterize a face, the performance of these local descriptors remain low by global descriptors (working on the entire image). The application of local descriptors (cutting image into blocks) must be able to store both the advantages of global and local methods in the Discrete Cosine Transform (DCT) domain. This system uses neural network techniques. The letter method provides a good compromise between the two approaches in terms of simplifying of calculation and classifying performance. Finally, we compare our results with those obtained from other local and global conventional approaches.

Keywords: Face detection, face recognition, discrete cosine transform (DCT), FPLBP, TPLBP, NN.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109816

References:

[1] V. N. Vapnik, "The Nature of Statistical Learning Theory", 2nd ed., Springer-Verlag New York Inc., 314 pages, 2000.
[2] S. Z. Li, A. K. Jain, Handbook of Face Recognition. Springer, 2005.
[3] P.N. Belhumeur, J.P. Hespanha and D.J. Kriegman, Eigenfaces vs fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 19(7):711– 720, Juillet 1997.
[4] A. Mellakh, A. Chaari., S. Guerfi& all., 2D Face Recognition in the IV2 Evaluation Campaign. Conf. on Advanced Concepts for Intelligent Vision Systems (ACIVS), Bordeaux, France, Octobre 2009.
[5] X. Zhang and Y. Jia, Face Recognition Based on Steerable Feature and Random Subspace LDA. Inter. workshop on Analysis and Modelling of Faces and Gestures (AMFG), 3723(2):170-183, Pekin, Chine, Octobre 2005.
[6] M. El Aroussi, M. El Hassouni, S. Ghouzali, M. Rziza1 and D. Aboutajdine, Novel face recognition approach based on steerable pyramid feature extraction. IEEE Inter. Conf. On Image Processing (ICIP), 4165 – 4168, Caire, Egypte, Novembre 2009.
[7] Y. Su, S.G. Shan, X.L. Chen, and W. Gao, Hierarchical ensemble of global and local classifers for face recognition. Journal of IEEE Trans. on Image Processing, IEEE, 18(8): 1885-1896, Juin 2009.
[8] W. Zuo, K. Wang, D. Zhang and H Zhang, Combination of two novel LDA-based methods for face recognition. Journal of Neurocomputing, Elsevier, 70 (4-6): 735-742, 2007.
[9] M. Turk and A. Pentland. Face recognition using eigenfaces. In Proc. Intl. Conf. On Computer Vision and Pattern Recognition, 1991.
[10] B Scholkopf, Asmola, and KR Muller. Nonlinear component analysis as a kernel eigenvalue problem. Technical Report No 44, December 1996.
[11] M. Bartlett, J. Movellan, and T. Sejnowski. Face recognition by independent component analysis. IEEE Transactions on Neural Networks, Vol. 13, No. 6, pp. 1450-1464, 2002.
[12] Daniel L. Swets and John (Juyang) Weng. Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence,18(8) :831-836, 1996.
[13] W. Chen, M. J. Er and S. Wu, PCA and LDA in DCT domain, Pattern Recognition Letters, Volume 26,Issue 15, 2005, pp. 2474 2482.
[14] Michael David Kelly. Visual identification of people by computer. PhD thesis, Stanford,CA, USA, 1971.
[15] KANADE, T. 1973. Computer recognition of human faces. Birkhauser, Basel, Switzerland, and Stuttgart, Germany.
[16] Brunelli, R., and Poggio, T., Face Recognition: Features versus Template, IEEE Trans. Pattern Anal. Machine Intell., vol. 15, pp.1042- 1052, 1993.
[17] D. H. Hubel and T. N. Wiesel, Functional architec- ture of macaque monkey visual cortex, Proc. Royal Soc. B (London), vol. 198, pp.1-59, 1978.