Face recognition by combining Gabor wavelets and nearest neighbor discriminant analysis

One of the successful approaches in face recognition is the Gabor wavelets-based approach. The importance of the Gabor wavelets lie under the fact that the kernels are similar to the 2-D receptive field profiles of the man visual neurons, offering spatial locality, spatial frequency and orientation selectivity. In this work, we propose a new combination of a Gabor wavelets-based method for illumination and expression invariant face recognition. It applies the Nearest Neighbor Discriminant Analysis to the augmented Gabor feature vectors obtained by the Gabor wavelets representation of facial images. To make use of all the features provided by different Gabor kernels, each kernel output is concatenated to form an augmented Gabor feature vector. The feasibility of the proposed method has been successfully tested on Yale database by giving a comparison with its predecessor, NNDA. The effectiveness of the method is shown by a comparative performance study against standard face recognition methods such as the combination of Gabor and Eigenfaces and the combination of the Gabor and Fisherfaces, using a subset of the FERET database containing a total of 600 facial images of 200 subjects exhibiting both illumination and facial expression variations. The achieved recognition rate of 98 percent in the FERET test shows the efficiency of the proposed method.