Data preparation for sample-based face detection

Face recognition is one of the most active biometric traits. Sample-based face detection is an active research topic in this area. It is of great interest to enhance the performance of the current face detection methods. In this paper, we propose new algorithms of sample selection and active samples generation to solve the problem of imbalanced training samples in the face detection task. Experimental results show that our proposed approaches enhance the performance of face detection, and the accuracy gain of the proposed approaches is significant when the available training samples are imbalanced or insufficient.

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