It is evident that the process of face recognition, by definition, should be based on the content of a face. The problem is: what is a "face"? Recently, a state-of-the-art statistics-based face recognition system, the PCA plus LDA approach, has been proposed [1]. However, the authors used "face" images that included hair, shoulders, face and background. Our intuition tells us that only a recognition process based on a "pure" face portion can be called face recognition. The mixture of irrelevant data may result in an incorrect set of decision boundaries. In this paper, we propose a statistics-based technique to quantitatively prove our assertion. For the purpose of evaluating how the different portions of a face image will influence the recognition results, a hypothesis testing model is proposed. We then implement the above mentioned face ...

In this paper, a coarse-to-fine, LDA-based face recognition system is proposed. Through careful implementation, we found that the databases adopted by two state-of-the-art face recognition systems [4, 5] were incorrect because they mistakenly use some none-face portions for face recognition. Hence, a face-only database is used in the proposed system. Since the facial organs on a human face only differ slightly from person to person, the decision-boundary determination process is tougher in this system than it is in conventional approaches. Therefore, in order to avoid the above mentioned ambiguity problem, we propose to retrieve a closest subset of database samples instead of retrieving a single sample. The proposed face recognition system has several advantages. First, the system is able to deal with a very large database and can thus provide a basis for efficient search. Second, due to its design nature, the system can handle the defocus and 1 This work was partially sup...

Face recognition, by definition, should be a recognition process in which recognition is based on the content of a face. The problem is: what is a "face"? Goudail et al. [1] and Swets and Weng [2] have recently proposed state-of-the-art statistics-based face recognition systems. However, they used "face" images that included hair, shoulders, face and background. Our intuition tells us that only a recognition process based on a "pure" face portion can be called face recognition. The mixture of irrelevant data may result in an incorrect set of decision boundaries. In this paper, we propose a statisticsbased technique to quantitatively prove our assertion. For the purpose of evaluating how the different portions of a face image will influence the recognition results, two hypothesis testing models are proposed. We then implement the two above mentio...