Performance evaluation of face recognition algorithms on the Asian face database, KFDB Human face is one of the most common and useful keys to a person’s identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA(Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching algorithms on the KFDB (Korean Face Database) which contains Korean face images taken under various conditions.