Face recognition based on the second-generation curvelet transform domain and KPCA

Abstract

Since wavelet transform can not fully describe facial curves features, in this paper, we propose a novel face recognition method based on Curvelet domain and kernel principal component analysis (KPCA). Using multi-scale, multi-directional Curvelet transform to extract image features not only has higher approximation accuracy and better performance of sparse expression, but also can effectively express the singularity along the curve. Furthermore, kernel principal component analysis (KPCA) is used to project Curvelet feature coefficient into kernel space with more expressing capability. Finally, the nearest method is adopted to classify. The results indicate that the algorithm is effective in image dimension reduction and face recognition rate in the JAFFE face database, ORL face database and FERET face database. © 2011 Springer-Verlag Berlin Heidelberg.