Detector of facial landmarks

In this thesis we develop a detector of facial landmarks based on the Deformable Part Models. We treat the task of landmark detection as an instance of the structured output classification problem. We propose to learn the parameters of the detector from data by the Structured Output Support Vector Machines algorithm. In contrast to previous works, the objective function of the learning algorithm is directly related to the performance of the resulting detector which is controlled by a user-defined loss function. The resulting detector is real-time on a standard PC, simple to implement and it can be easily changed for detection of a different set of landmarks. We evaluate performance of the proposed landmark detector on a challenging “Labeled Faces in the Wild” database. The empirical results demonstrate that the proposed detector is consistently more accurate than two public domain implementations based on the Active Appearance Models and the Deformable Part Models. We provide an open source implementation of the proposed detector as well as the algorithm for supervised learning of its parameters from data.