Face Recognition Based on Curvefaces

Abstract

A new method called curvefaces was firstly presented for face recognition, which is based on curvelet transform. Curvelet is the latest multiscale geometric analysis tool. Contrast to wavelet transform, curvelet transform directly takes edges as the basic representation elements and is anisotropic with strong direction. It is a multiresolution, band pass and directional function analysis method which is useful to represent the image edges and the curved singularities in images more efficiently. It yields a more sparse representation of the image than wavelet and ridgelet transform. In face recognition, the curvelet coefficients can better represent the main features of the faces. The support vector machine (SVM) can then be used to classify the images. SVM is based on the statistical learning theory and is especially valid for small sample set and can get high recognition rate. Multi-class SVM is employed in this paper. The simulation shows that the proposed method is better than wavelet based method.