Face tracking algorithm combing color and texture features based on particle filter

In this paper, a human face tracking algorithm combing color and texture features based on particle filter is proposed. The proposed approach makes use of the characteristics of particle filter which not only can effectively deal with nonlinear and non Gaussian process but also can combine multiple face features information. Different robustness to different environments of features is considered and the weighted color histogram and rotated complex wavelet filter (RCWF) are used to describe features. Thus the color and texture features can be fused under particle filter framework to develop the new face tracking algorithm. Experimental results demonstrate that, compared with the method based on single feature, the robustness, accuracy and flexibility of the algorithm have the better effectiveness of the performance, and the experiments also show the accuracy for face tracking in real scenes.