A real time face tracking system using rank deficient face detection and motion estimation

In this paper, we present a novel real-time face tracking system using rank deficient face detection. Real time computer vision systems need to be fast and efficient to be of practical application. Moreover, the system should be adaptable to diverse real life situations with varied availability of resources. We detect human face in an input grayscale image using a rank deficient face detection algorithm, which has been shown to be 4 to 6 times faster than unconstrained reduced set systems. The algorithm works on grayscale images and so easily adapts to change in ambient conditions like lighting conditions and different people, and is compatible with both color and grayscale cameras. One major problem in face tracking system used in environments with multiple candidate faces is which face to track and how to keep tracking a heuristically selected face without actually involving face authentication procedures. We have developed a highly flexible yet robust algorithm to deal with these problems. In case of multiple faces in the frame, the most prominent face is identified and automatically selected for tracking, and Webcam focused on the desired face. Motion estimation and compensation is then incorporated to ensure robust tracking, to minimize false detections, and for persistent tracking of the desired face. The system showed a robust 10 fps and a false detection rate of 0.6% on a 1.4 GHz Pentium IV workstation using an Intel CS110 Webcam. The principle used in this system can be applied to real time tracking of other objects, viz. cars, the human hand, etc. by using an appropriate training set. It can also be extended to provide face authentication and subsequent tracking of desired face.