Fuzzy system-based real-time face tracking in a multi-subject environment with a pan-tilt-zoom camera

This paper proposes real-time face tracking in a multi-subject environment with a pan-tilt-zoom camera using the fuzzy system technique. Tracking is based on detected faces in the Hue–Saturation–Value (HSV) color space. To detect faces, a fuzzy classifier segments skin colors in the HS color space. To reduce the influence of illumination, a fuzzy system is designed to adaptively determine the fuzzy classifier segmentation threshold according the V color space of an image. Detected skin regions are considered face candidates. Shape and color features serve as another fuzzy classifier inputs, leading to a final detection. For face tracking, a Kalman filter algorithm predicts face detection regions and corrects tracking trajectory. When no face is detected, the consecutive frame difference is employed to track the moving person to avoid tracking lost. To track a specific person among multiple persons, a clothing color histogram in the HS space serves as the determination criterion. The performance of the proposed face detection method is compared to other real-time face detection methods. In real-time operations, the tracking system uses camera panning, tilting, and zooming operations to keep the tracked person within the camera view and maintain a suitable face size in the image.