@article{(Open Science Index):https://publications.waset.org/pdf/6439,
	  title     = {Local Curvelet Based Classification Using Linear Discriminant Analysis for Face Recognition},
	  author    = {Mohammed Rziza and  Mohamed El Aroussi and  Mohammed El Hassouni and  Sanaa Ghouzali and  Driss Aboutajdine},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {4},
	  year      = {2009},
	  pages     = {1090 - 1095},
	  ee        = {https://publications.waset.org/pdf/6439},
	  url   	= {https://publications.waset.org/vol/28},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 28, 2009},