Abstract—Biometric face Recognition is a new generation technology for identification and verification. Many techniques have been developed for this purpose in past two decades. A good face recognition technique must require unique, effective and efficient features from face images. Content based image retrieval (CBIR) can extract features that can be useful for face recognition. Recently, Gabor and curvelet texture features have been successfully used in image retrieval research. In this paper, we propose a novel face recognition method that uses texture features obtained by calculating mean and standard deviation of Gabor and curvelet transformed face images. PCA is then applied to the feature vectors instead of entire transformed images which traditional methods do. Using this process, we build four classifiers using mean and standard deviation calculated from Gabor and curvelet transformed face images. For identification purpose, a new matching strategy is proposed that checks goodness of four matching results of the classifiers. As we consider only mean or standard deviation features, the image representation has comparatively lower dimensions. Furthermore, our proposed method does not necessarily require all the input images to be of same resolution. We evaluate the proposed method using ORL and Yale face databases. The recognition results of the experiments show that our approach is significantly better than the conventional methods.

Index Terms—Content based image retrieval (CBIR); gaborand curvelet transform; principal component analysis (PCA).

The authors are with the Department of Computer Science and Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka- 1000, Bangladeshs

Cite: Shafin Rahman, Sheikh Motahar Naim, Abdullah Al Farooq, and Md. Monirul Islam, "Combination of Gabor and Curvelet Texture Features for Face Recognition Using Principal Component Analysis," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 264-269 , 2012.