Multiple-cue face tracking using particle filter embedded in incremental discriminant models

This paper presents a multi-feature integrated algorithm incorporating a particle filter and the incremental linear discriminant models for face tracking purposes. To solve the drift problem, the discriminant models are constructed for colour and orientation feature to separate the face from the background clutter. The colour and orientation features are described in the form of part-wisely concatenating histograms such that the global information and local geometry can be preserved. Additionally, the proposed adaptive confidence value for each feature is fused with the corresponding likelihood probability in a particle filter. To render the face tracking system more robust toward variations in the facial appearance and background scene, the LDA model for each feature is updated on a frame-by-frame basis by using the discriminant feature vectors selected in accordance with a co-training approach. The experimental results show that the proposed system deals successfully with face appearance variations (including out-of-plane rotations), partial occlusions, varying illumination conditions, multiple scales and viewpoints, and cluttered background scenes.