Efficient approach for face detection in video surveillance

Security access control systems and automatic video surveillance systems are becoming increasingly important recently, and detecting human faces is one of the indispensable processes. In this paper, an approach is presented to detect faces in video surveillance. Firstly, both the skin-color and motion components are applied to extract skin-like regions. The skin-color segmentation algorithm is based on the BPNN (back-error-propagation neural network) and the motion component is obtained with frame difference algorithm. Secondly, the image is clustered into separated face candidates by using the region growing technique. Finally, the face candidates are further verified by the rule-based algorithm. The experiment results demonstrate that both the accuracy and processing speed are very promising and the approach can be applied for the practical use.