Performance analysis of face recognition algorithms on Korean face database

Abstract

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 caused by variable illuminations, expressions, poses and accessories. To analyze these face recognition algorithm, it is indispensable to collect various data as much as possible. Face databases such as CMU PIE (USA), FERET (USA), AR Face DB (USA) and XM2VTS (UK) are the representative ones commonly used. However, many databases do not provide adequately annotated information of the pose angle, illumination angle, illumination color and ground-truth. Mostly, they do not include large enough number of images and video data taken under various environments. Furthermore, the faces on these databases have different characteristics from those of Asian. Thus, we have designed and constructed a Korean Face Database (KFDB) which includes not only images but also video clips, ground-truth information of facial feature points and descriptions of subjects and environment conditions so that it can be used for general purposes. In this paper, we present the KFDB which contains image and video data for 1920 subjects and has been constructed in 3 years (sessions). We also present recognition results by CM (Correlation Matching) and PCA (Principal Component Analysis) which are used as baseline algorithms upon CMU PIE and KFDB, so as to understand how recognition rate is changed by altering image taking conditions.