A robust face detection system for real environments

In this paper, a robust real-time face detection system based on the integration of different location methods is proposed. A hierarchical architecture composed of three levels is designed. At the first level, a change detection method is applied to detect blobs of moving objects (i.e., humans) in the scene. Then, the silhouette of each blob is analyzed to focalize the attention of the system on small image areas where the probability of finding human heads is high. At the second level, two different methods, i.e., the skin color and the principal component analysis, are applied to locate human faces. Finally, the higher level fuses the obtained location data to improve the face detection reliability. The system is tested in outdoor environments in the context of a video-based surveillance system.