Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30737
Local Curvelet Based Classification Using Linear Discriminant Analysis for Face Recognition

Authors: Driss Aboutajdine, Mohammed Rziza, Mohamed El Aroussi, Mohammed El Hassouni, Sanaa Ghouzali


In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA, and independent component Analysis (ICA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet-LDA algorithm. Experimental results on ORL, YALE and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.

Keywords: Linear Discriminant Analysis (LDA), DWT, contourlet, curvelet, Discreet Wavelet Transform, Block-based analysis, face recognition (FR)

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1416


[1] T. Mandal, A. Majumdar, and Q.M. Jonathan Wu.,Face Recognition by Curvelet Based Feature Extraction, ICIAR 2007, LNCS 4633, pp. 806817, 2007.
[2] A. Majumdar, Bangla Basic Character Recognition Using Digital Curvelet Transform, Journal of Pattern Recognition Research JPRR 2007, vole 1, 2007,pp 17-26.
[3] W. R. Boukabou, A Bouridane, Contourlet-Based Feature Extraction with PCA for Face Recognition, In: NASA/ESA Conference on Adaptive Hardware and Systems 2008 . 2008 IEEE.
[4] M.S. Bartlett, H.M. Lades and T.J. Sejnowski, Independent component representations for face recognition, in Proceedings of the SPIE, vol. 3299, 1998, pp 528-539.
[5] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19(7), Jul. 1997, pp 711-720.
[6] J.L. Starck, M. Elad, D. Donoho, Redundant multiscale transforms and their application for morphological component separation, Adv. Imaging Electron Phys. 132 (2004),pp 287348.
[7] A. Mojsilovic, M. Popovic, S. Markovic, M. Krstic, Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform, IEEE Trans. Med. Imaging . vol 17 (4) 1998, pp 541549.
[8] B.S. Manjunath, P. Wu, S. Newsam, H.D. Shin, A texture descriptor for browsing and similarity retrieval, Signal Process.: Image Commun. vol. 16(1), 2000, pp 3343.
[9] Belbachir, A.N., Goebel, P.M., The Contourlet Transform for Image Compression. Physics in Signal and Image Processing, Toulouse, France (January 2005)
[10] Li, A., Li, X., Wang, S., Li, H., A Multiscale and Multidirectional Image Denoising Algorithm Based on Contourlet Transform. International Conference on Intelligent Information Hiding and Multimedia, pp. 635638 (2006)
[11] E. J. Candes and D. L. Donoho. Curvelets: A surprisingly effective nonadaptive representation for objects with edges,
[Online]. Available 2000.
[12] M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, vol. 3(1), Mar. 1991, pp 71-86.
[13] G.C. Feng, D. Q. D., P. C. Yuen. Human face recognition using pca on wavelet subband. Journal of Electronic Imaging, 9(2):226233, 2000.
[14] J. T. Chien, C. C. W. Discriminant waveletfaces and nearest feature classi?ers for face recogniton. IEEE Trans.PAMI, 24(2):16441649, 2002.
[15] M. Zhao, Z. L., P. Li. Face recognition based on wavelet transform weighted modular pca. Proc. Congress in Image and Signal Processing, 2008.
[16] Bai-Ling Zhang, S. S. G., Haihong Zhang. Face recognition by applying wavelet subband repre- sentation and kernel associative memory. IEEE Trans. Neural networks, 15(1):166177, 2004.
[17] Tanaya Mandal, Q. J. W. and Yuan, Y. Curvelet based face recognition via dimension reduction. To appear in: Signal Processing, 2009.
[18] M. El Aroussi, S. Ghouzali, M. El Hassouni, M .Rziza, and D. Aboutajdine, Curvelet-Based Feature Extraction with B-LDA for Face Recognition, The 7th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)., 2009.
[19] Phillips, P.J.,Moon, H., Rauss, P.J., Rizvi, S. The FERET evaluation methodology for face recognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, no. 10, October 2000
[20] W. Zhao, R. Chellappa, P.J. Phillips and A. Rosenfeld, Face recognition: A literature survey, ACM Computing Survey, vol. 34(4), 2003, pp 399- 485.
[21] E. Stollnitz, T. DeRose, D. Salesin, D, Wavelets for computer graphics:a primer part 1, IEEE Comput. Graph. Appl., vol. 15 (3), (1995), pp 7684.
[22] C. Mulcahy, Image compression using the Haar wavelet transform, it Spelman Sci. Math J., vol 1, (1997), pp 2231.
[23] R.A. DeVore, B. Jawerth, and B. J. Lucier, Image compression through wavelet transform coding, IEEE Trans. Inform. Theory (Special Issue on Wavelet Transforms and Multiresolution Signal Analysis), vol. 38, pp. 719-746, Mar. 1992.
[24] Garcia, C., Zikos, G. & Tziritas, G. A wavelet-based framework for face recognition, Proc of theWorkshop on Advances in Facial Image Analysis and Recognition Technology, 5th European Conference on Computer Vision (ECCV-98) , pp. 84-92, Freiburg Allemagne.