A robust real-time face tracking algorithm

This paper presents a statistics-based method for estimating the position and size of a face in a complex background. Face position and size are estimated based on robust statistical measurements which are derived from two one-dimensional histograms obtained by projecting the result of skin color filtering. The proposed algorithm also utilizes a linear Kalman filter and a simple nonlinear filter to perform smooth tracking and remove jitter. The algorithm has been implemented and tested under a wide range of real-world conditions. It has consistently provided performance which satisfies the following requirements: (1) the ability to automatically determine the initial position and size of a face and track it in a complex background; (2) is insensitive to partial occlusions and shadows; (3) is insensitive to face orientation and scale changes; and (4) is also insensitive to lighting condition changes. In addition, the algorithm is computationally simple so that it can be executed in real-time.