An Efficient Face Recognition Method by Fusing Spatial Discriminant Facial Features

Abstract

Feature level fusion is a very well known technique for improving 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 lying along the horizontal, vertical and diagonal directions, so that this fused feature vector can contain more discriminant information than the individual facial feature of single direction only. However due to the fusion of features the size of fused feature vector becomes larger, which may increase complexity of the classifier to be used for recognition. To optimize this lower dimensional discriminant features are again extracted from this large fused feature vector. In our experiment we have applied 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 feature matrices are then fused to form a large feature matrix. The dimension of this large fused matrix is then further reduced by G-2DFLD method and this resultant matrix 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.