In this talk, a coarse-to-fine, LDA-based face recognition system is proposed. Through careful implementation, we found that the database adopted by two state-of-the-art face recognition systems were incorrect because they mistakenly use some nonface portions for face recognition. We shall prove the above mentioned problem thru a statistics-based method. Since the irrelevant data cannot be included for face recognition, a face-only database is used in the proposed system. On the other hand, 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. In order to avoid the 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 noise problems. Third, the system is faster than the autocorrelation plus LDA approach and the PCA plus LDA approach, which are believed to be two state-of-the-art face recognition systems. Experimental results prove that the proposed method is better than traditional methods in terms of efficiency and accuracy.