A cascade face recognition system using hybrid feature extraction
Digital Signal Processing
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Face detection and recognition in a video is a challenging research topic as overall processes must be done timely and efficiently. In this paper, a novel face detection and recognition system using three fast cascade face verification modules and an ensemble classifier is presented. Firstly, the head of a tester is serially verified by our proposed three verification modules: face skin verification module, face symmetry verification module, and eye template verification module. The three verification modules can eliminate the tilted faces, the backs of the head, and any other non-face moving objects in the video. Only the frontal face images are sent to face recognition engine. The frontal face detection reliability can be adjusted by simply setting the verification thresholds in the verification modules. Secondly, three hybrid feature sets are applied to face recognition. An ensemble classifier scheme is proposed to congregate three individual Artificial Neural Network (ANN) classifiers trained by the three hybrid feature sets. Experiments demonstrated that the frontal face detection rate can be achieved as high as 95% in the low quality video images. The overall face recognition rate and reliability are increased at the same time using the proposed ensemble classifier in the system.