Face Tracking in Video by Using Kalman Filter

Face Tracking has been one of the most studied topics in computer vision literature. Facial feature extraction has some problems which must be researched. Small variations of face size and orientation can affect the result of face tracking. Since the input image is captured from a surveillance camera, certain conditions have to be considered like different levels of brightness, shadows and clearness which are challenges for detection and tracking purpose. Most facial feature extraction methods are sensitive to various non-ideal such as variation in illumination, noise, orientation, time-consumption and color space used. So there is a need for a good feature extraction method that will enhance the quality and performance of face recognition system. First, segmentation of foreground and background object is the one by using histogram equalization. By this method we are able to segment face based on skin color. After segmenting, Kalman filter is used to track the faces under several conditions. This feature is helpful for the development of a real-time visual tracking control system. Keywords— Face detection, Facial Feature Extraction, Kalman Filter, Histogram Equalization, Face Tracking.