Constrained Ensemble Initialization for Facial Landmark Tracking in Video

摘要

Accurate and robust facial landmark tracking is a crucial step for face recognition and affect analysis systems. We often want to not only detect facial landmarks in images but to be able to track them reliably and consistently over time. Recently there has been an increase in research interest in facial landmark detection, especially in cascaded regression based methods such as the Supervised Descent Method (SDM). However, while facial landmark detection in images has improved significantly, comparably very little attention has been given to the task of landmark detection/tracking in videos. In our work we present a novel initialization procedure that can help with cascaded regression based facial landmark detection and tracking. Our initialization technique exploits the fact that cascaded regression is sensitive to initialization noise, especially in the presence of out-of-plane head pose variation, e.g. when a person is looking down when reading or during fast head motion. Our approach allows to learn good candidates for initialization, that we exploit in our tracking framework. We evaluate our technique on 300VW dataset - a large publicly available corpus of in-the-wild videos and demonstrate its effectiveness for a number of cascaded-regression landmark detection approaches.