Adaptive facial feature extraction

We present a method which is able to adapt from a generic facial representation to a person-specific model of a face. It is referred to as Adaptive Constrained Polynomial Trees (ACPT). Especially in vehicle driving scenarios, special assumptions can be made. A generic facial representation which is able to handle many different persons can be specialized to the current driver to cope with his/her individual face and his/her individual facial features. This leads to a more robust extraction of specified points in the face like nose tip or mouth corners. The proposed method is trained on the LFPW and tested on the FGnet “talking face” dataset. It can be shown, that the presented adaptive model is able to outperform the presented generic facial representation approach. These promising results can be used for further analysis of the driver.