Performance of biometric systems is dependent on quality of acquired biometric samples. Poor sample quality is a main reason for matching errors in biometric systems and may be the main weakness of some implementations. In this paper, we present an approach for standardization of facial image quality, and develop facial symmetry based methods for its assessment by which facial asymmetries caused by non-frontal lighting and improper facial pose can be measured. Experimental results are provided to illustrate the concepts, definitions and effectiveness.

Abstract. Many applications require tracking and recognition of multiple faces at distances, such as in video surveillance. Such a task, dealing with noncooperative objects is more challenging than handling a single face and than tackling a cooperative user. The difficulties include mutual occlusions of multiple faces and arbitrary head poses. In this paper, we present a method for solving the problems and a real-time system implementation. An appearance model updating mechanism is developed via Gaussian Mixture Models to deal with tracking under head rotation and mutual occlusion. Face recognition based on video sequence is then performed to get the identity information. Through fusing the tracking and recognition information, the performance of them are both improved. A real-time system for multi-face tracking and recognition at distances is presented. The system can track multiple faces under head rotations, and deal with total occlusion effectively regardless of the motion trajectory. It is also able to recognize multi-persons simultaneously. Experimental results demonstrate promising performance of the system.