Face Recognition with Integrating Multiple Cues

Automatic face recognition is a challenge task, especially working in practical uncontrolled environments. Over the past two decades, numerous innovative ideas and effective processing approaches had been proposed and developed, e.g. various normalization techniques, intrinsic feature extractions and representation schemes, machine learning methods and recognition mechanisms etc. Those approaches based on different principles had been shown possessing varying degrees of effectiveness in different aspects. It is expected that the techniques of information fusion with integrating the advantages of existing methods will boost the recognition performance. This paper deals with developing effective approaches for face recognition using information fusion techniques based on integrating multiple cues. The multiple stage integrating techniques dedicated to localization of landmark points and pose estimation were presented. The precise data of localization of landmarks and pose estimation provide the essential geometry basics for further processing. A face recognition classifier scheme with integration of multiple feature representation and multiple block region scores is also proposed. The experiment results show that the proposed approach can reduce equal error rate EER significantly, compared with using single feature and single block representations. The proposed approach had been shown possessing the best performance in participating MCFR2011 competition.