Real-time face detection and tracking for mobile videoconferencing

This paper addresses the issue of face detection and tracking in the context of a mobile videoconferencing application. While the integration of such technology into a mobile videophone is advantageous, allowing face stabilization, reduced bandwidth requirements and smaller display sizes, its deployment in such an environment may not be straightforward, since most face detection methods reported in the literature assume at least modest processing capabilities and memory and, usually, floating-point capabilities. The face detection and tracking method which is presented here achieves high performance, robustness to illumination variations and geometric changes, such as viewpoint and scale changes, and at the same time entails a significantly reduced computational complexity. Our method requires only integer operations and very small amounts of memory, of the order of a few hundred bytes, facilitating a real-time implementation on small microprocessors or custom hardware. In this context, this paper will also examine an FPGA implementation of the proposed algorithmic framework which, as will be seen, achieves extremely high frame processing rates at low clock speeds.