Face Recognition by Fusing Local and Global Discriminant Features

This paper presents a novel scheme for face recognition by fusing local and global discriminant features. It has been observed that facial changes are occurred due to variations in facial expression, illumination condition, pose, etc. and these changes are often appeared only some regions of the whole image. The global features extracted from the whole image are not able to cope with these facial changes. To cope with the above facial changes face images are divided into a number of non-overlapping smaller sub-images and discriminant features are extracted from these sub-images as well as from the whole image. All these extracted local and global features are fused to form a large feature vector. We have used generalized two-dimensional fisher's linear discriminate (G-2DFLD) method to extract these local and global discriminant features. We have used the fisher's linear discriminate (FLD) method to extract lower dimensional discriminant features from the fused large feature vector. A Multi-class Support Vector Machine (SVM) is applied on these reduced feature vector for classification. The proposed method was evaluated on AT&T Face Database and experimental results show that the performance of the proposed method is better than other global feature extraction methods like PCA, 2DPCA, PCA+FLD, 2DFLD and G-2DFLD methods.