CAMShift face tracking with adaptive MB-LBP pre-filter

Since the color probability distribution based continuous adaptive mean shift (CAMShift) face tracking algorithm is simple and easy to be implemented, it is widely used in real-time tracking applications. However, because CAMShift takes skin color histogram as the tracking model, its tracking is easy to fail when the target locates in a skin-color-like background region. For that, a CAMShift face tracking algorithm using adaptive Multi-Block Local Binary Pattern (MB-LBP) pre-filter is proposed in this paper. Firstly, a cascade MB-LBP node classifiers, which can well detect the basic characteristics of face, is trained. If the tracking window enters a skin-color like background region, this pre-filter is then adaptively inserted to eliminate the skin-like-color background interferences. Consequently, the robustness of the tracking algorithm is improved. Experimental results have proved the superior tracking ability of the proposed algorithm under skin-color-like background interferences.