Regression-based Active Appearance Model initialization for facial feature tracking with missing frames

The Active Appearance Model (AAM) is receiving considerable attention in the field of facial analysis as a powerful method for modeling and segmenting deformable visual objects. Several extensions and improvements have been proposed on the original AAM, but AAMs maintain their dependence on the good initialization of model parameters to achieve accurate fitting results. AAMs are usually used directly in video tracking by searching on each subsequent frame that employs the fitting result of the previous frame for initialization. However, this model sometimes fails when large movements exist between two frames. This mechanism occurs when frames are dropped from the video due to the use of a lossy multimedia network. A regression-based approach for automatic AAM initialization is presented in this paper. After undergoing a scattered feature correspondence based on a dual-threshold matching strategy, the AAM shape points are initialized by the spatial map between local-landmark (L2L) correspondences. The map is learned based on Kernel Ridge Regression (KRR). The proposed method can successfully track the frames that are not identified with the general AAM trackers by establishing spatial relationship between local and landmark points. The initialization is robust to disturbances, which enables it to outperform key-feature-tracking or detection-based methods. We demonstrate the efficacy of the approach on two challenging facial videos with different training data and report a detailed quantitative evaluation of its performance.