Facial Landmarks Detector Learned by the Structured Output SVM

Abstract

We propose a principled approach to supervised learning of facial landmarks detector based on the Deformable Part Models (DPM). We treat the task of landmarks detection as an instance of the structured output classification. To learn the parameters of the detector we use the Structured Output Support Vector Machines algorithm. The objective function of the learning algorithm is directly related to the performance of the detector and controlled by the user-defined loss function, in contrast to the previous works. Our proposed detector is real-time on a standard computer, simple to implement and easily modifiable for detection of various set of landmarks. We evaluate the performance of our detector on a challenging “Labeled Faces in the Wild” (LFW) database. The empirical results show that our detector consistently outperforms two public domain implementations based on the Active Appearance Models and the DPM. We are releasing open-source code implementing our proposed detector along with the manual annotation of seven facial landmarks for nearly all images in the LFW database.