Face Tracking Using Robust Statistical Estimation

This paper presents a statistics-based method for estimating the position and size of a face in 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)able to automatically determine the initial position and size of a face and track it in complex background; 2)insensitive to partial occlusions and shadows; 3)insensitive to face orientation and scale changes; 4)insensitive to lighting condition changes; and 5)computationally simple enough to be executed in real-time.