abstract
In this paper, we propose a new feature extraction approach for face recognition based on Curvelet transformand local binary pattern operator. Themotivation ofthis approach is based on two observations. One is that Curvelet transform is a new anisotropic multi-resolution analysis tool, which can effectively represent image edge discontinuities; the other is that local binary pattern operator is one of the best currenttexture descriptors for face images.As the curveletfeatures in differentfrequency bands represent different information of the original image, we extract such features using different methods for different frequency bands. Technically, the lowest frequency band component is processed using the local binary patternmethod, andonly themediumfrequency bandcomponents arenormalized.Andthen, we combine them to create a feature set, and use the local preservation projection to reduce its dimension. Finally, we classify the test samples using the nearest neighbor classifier in the reduced space. Extensive experiments on the Yale database, the extended Yale B database, the PIE pose 09 database, and the FRGC database illustrate the effectiveness of the proposed method.
