Adaptive Appearance Model and Condensation Algorithm for Robust Face Tracking

Abstract

We present an adaptive framework for condensation algorithms in the context of human-face tracking. We attack the face tracking problem by making factored sampling more efficient and appearance update more effective. An adaptive affine cascade factored sampling strategy is introduced to sample the parameter space such that coarse face locations are located first, followed by a fine factored sampling with a small number of particles. In addition, the local linearity of an appearance manifold is used in conjunction with a new criterion to select a tangent plane for updating an appearance in face tracking. Our proposed method seeks the best linear variety from the selected tangent plane to form a reference image. We demonstrate the effectiveness and efficiency of the proposed method on a number of challenging videos. These test video sequences show that our method is robust to illumination, appearance, and pose changes, as well as temporary occlusions. Quantitatively, our method achieves the average root-mean-square error at 4.98 on the well-known dudek video sequence while maintaining a proficient speed at 8.74 fps. Finally, while our algorithm is adaptive during execution, no training is required.