Performance evaluation of face recognition algorithms on Asian face database

Human face is one of the most common and useful keys to a person´s identity. Although, a number of face recognition algorithms have been proposed, many researchers believe that the technology should be improved further in order to overcome the instability due to variable illuminations, expressions, poses and accessories. In general, face databases for European and American such as CMU PIE (USA), FERET (USA), AR Face DB (USA) and XM2VTS (UK) have been used for training face recognition algorithms and testing the performance of those. However, many of the images in databases are not adequately annotated with the exact pose angle, illumination angle and illuminant color. Also, the faces on these databases have definitely different characteristics from those of Asian. Thus, we constructed the well-designed Korean face database (KFDB), which includes not only images but also ground truth information for facial feature points, and description files for subjects and exact capture environments. In this paper, we report the experimental results of face recognition performed using CM (correlation matching), PCA (principal component analysis) and LFA (local feature analysis) algorithms under various conditions on the KFDB.