Fusion of Directional Spatial Discriminant Features for Face Recognition

Feature level fusion is one of the most important techniques, used to improve the performance of a face recognition system. This paper presents an approach of fusion of directional spatial discriminant features for face recognition. The key idea of the proposed method is to fuse the facial features lie along the horizontal, vertical and diagonal directions. So that this fused feature vector can contain more discriminant information than the individual facial feature lie along single direction. However due to fusion the size of fused feature vector is become larger which may increase complexity of the classifier. To optimize this lower dimensional discriminant features are again extracted from this large fused feature vector. In our experiment, we apply G-2DFLD method on the original images to extract the discriminant features. Then original images are converted into diagonal images and another set of discriminant features, representing the diagonal information, are extracted by using the G-2DFLD method. The original and diagonal features vectors are then fused to form a large feature vector. The dimension of this large fused feature vector is then reduced by PCA method and this resultant reduced feature vector is used for classification and recognition by Radial Basis Function-Neural Networks (RBF-NN). Experiments on the AT&T (formally known as ORL database) face database indicate the competitive performance of the proposed method, as compared to some existing subspaces-based methods.Click here and insert your abstract text.