Robust real-time detection, tracking, and pose estimation of faces in video streams

Robust human face analysis has been recognized as a crucial part in intelligent systems. In this paper we present the development of a computational framework for robust detection, tracking, and pose estimation of faces captured by video arrays. We discuss the development of a multi-primitive skin-tone and edge-based detection module embedded in a tracking module for efficient and robust face detection and tracking. A continuous density HMM based pose estimation is developed for an accurate estimate of the face orientation motions. Experimental evaluations of these algorithms suggest the validity of the proposed framework and its computational modules.