Kernel-based face detection and tracking with adaptive control by Kalman filtering

This paper presents an approach to face detection and tracking in video sequences. We use a set of Haar-like features to train a cascade of classifiers. Face-tracking is performed using the mean-shift algorithm. We propose a method for adaptation both of the tracking-window and of the color-distribution model in order to increase robustness to illumination changes of the environment. Minimizing of the number of iterations is achieved by using dynamic prediction with Kalman filter.