Based On Discriminant Ordinary Vector Face Recognition,

As one of the most important applications of computer vision, pattern recognition and image processing, face recognition has recently received more and more extensive attention. Compared with other biometrics, face recognition technology is more acceptable because it is more natural, friendly and non-intrusive, but the accuracy of face recognition is not high enough at present.In face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. As a consequence, the within-class scatter matrix is singular and the Linear Discriminant Analysis(LDA) method cannot be applied directly. This problem is known as the "small sample size" problem. In this paper, a new face recognition method called the Discriminative Common Vector method is proposed. As a variation of Fisher's Linear Discriminant Analysis for the small sample size case, the proposed method yields an optimal solution for maximizing the modified Fisher's Linear Discriminant criterion given in the paper.The main contributions of this paper include two parts. One is feature extraction. The other is classification. For the first part, we extract the discriminative common vectors as feature vectors from the training set. Two different algorithms are given to extract these vectors representing each person in the training set of the face database. One algorithm uses the within-class scatter matrix of the samples in the training set while the other uses the subspace methods and the Gram-Schmidt orthogonalization procedure to obtain the discriminative common vectors. Then, the discriminative common vectors are used for classification of new faces. A nearest-neighbor algorithm is employed using the Euclidean distance for classification.The ORL face database are used to test the proposed method. We showed that every sample in a given class produces the same unique common vector when they are projected onto the null space of the within-class scatter matrix. Experimental results show that the Discriminative Common Vector method is superior to other methods in term of accuracy, real-time performance, storage requirements and numerical stability.