Real-time multi-view facial landmark detector learned by the structured output SVM

While the problem of facial landmark detection is getting big attention in the computer vision community recently, most of the methods deal only with near-frontal views and there is only a few really multi-view detectors available, that are capable of detection in a wide range of yaw angle (e.g. Φ ε (-90°, 90°)). We describe a multi-view facial landmark detector based on the Deformable Part Models, which treats the problem of the simultaneous landmark detection and the viewing angle estimation within a structured output classification framework. We present an easily extensible and flexible framework which provides a real-time performance on the “in the wild” images, evaluated on a challenging “Annotated Facial Landmarks in the Wild” database. We show that our detector achieves better results than the current state of the art in terms of the localization error.