Toward fast feature adaptation and localization for real-time face recognition systems

In a home environment, video surveillance employing face detection and recognition is attractive for new applications. Facial feature (e.g. eyes and mouth) localization in the face is an essential task for face recognition because it constitutes an indispensable step for face geometry normalization. This paper presents a new and efficient feature localization approach for real-time personal surveillance applications with low-quality images. The proposed approach consists of three major steps: (1) self-adaptive iris tracing, which is preceded by a trace-point selection process with multiple initializations to overcome the local convergence problem, (2) eye structure verification using an eye template with limited deformation freedom, and (3) eye-pair selection based on a combination of metrics. We have tested our facial feature localization method on about 100 randomly selected face images from the AR database and 30 face images downloaded from the Internet. The results show that our approach achieves a correct detection rate of 96%. Since our eye-selection technique does not involve time-consuming deformation processes, it yields relatively fast processing. The proposed algorithm has been successfully applied to a real-time home video surveillance system and proven to be an effective and computationally efficient face normalization method preceding the face recognition