Real-time face detection/identification for surveillance system

Face processing is one among the popular problems of detection and recognition. In surveillance system application, the massive data of video is analyzed by complex algorithms that must consider the real-time constraints. In this paper we propose a face processing framework as a component-based architecture for dealing with face online processing. Face detection and classification algorithms are realized based on a Haar-like features and support vector machine (SVM) respectively as a way to efficiently extract a face image and enhance face recognition rates with robustness on different orientation Experimental results are presented with discussion.