Weighted Sub-Gabor for face recognition

In this paper, we introduce a new face recognition approach based on the representation of each individual by a feature vector extracted through a bank of Gabor filters and Karhunen–Loeve transform. This method operates directly on sub-patterns of the whole image, extracting features from them. The features obtained by each sub-pattern are used to train a Parzen Window Classifier. Moreover, our method computes the contributions of each part in order to enhance the robustness to facial expression and illumination condition. Extensive experiments carried out on the FERET database of faces prove the advantages of the proposed approach when compared with other well-known techniques.