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, and its cost is relatively low, but the accuracy of Face Recognition is not high enough at present. Subspace Analysis is one of important methods of Statistical Pattern Recognition. The basic idea of Subspace Analysis is to find a subspace (feature space) of the original space (sample space), and classify the projected samples in the subspace. One advantage of this method is that the computational complexity is reduced because of the dimensionality reduction of the samples. More importantly, the classification of the samples will be easier in the subspace if we choose an appropriate subspace, so the accuracy ~f recognition is higher. We can get different subspace by using different criteria. In this paper we will introduce various Subspace Analysis methods (such as Principal Component Analysis, Linear Discriminant Analysis, Independent Component Analysis, and Kernel-based Subspace Analysis etc.) and their applications in Face Recognition. The main contribution of this paper is that various Subspace Analysis methods are compared and evaluated; a new simplified method based on the null space is proposed to solve the Small Sample Size Problem of Linear Discriminant Analysis; and Kernel-based Linear Discriminant Analysis method is used in Face Recognition and gets good performance. Some concepts about Face Recognition and Biometrics will be in- troduced in Chapter 1, followed by the significance, difficulties and evaluation criteria of Face Recognition research. Also an ideal Face Recognition system architecture is outlined in Chapter 1. Chapter 2 is a brief survey on representative Face Recognition methods. Chapter 3 introduces in detail various Subspace Analysis methods, especially our work, and their applications in Face Recognition. Chapter 4 summary the whole paper and give a prospect of the de- velopment of Face Recognition technology.
修改评论