Face Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Model
This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. In initialization stage, we propose a group sparse optimized mixture model to automatically select the most salient facial landmarks. By introducing 3D face shape model, we apply procrustes analysis to provide pose aware landmark initialization. In landmark localization stage, the first step uses mean shift local search with constrained local model to rapidly approach the global optimum. The second step uses component wise active contours to discriminatively refine the subtle shape variation. Our framework simultaneously handles face detection, pose robust landmark localization and tracking in real time. Extensive experiments are conducted on both laboratory environmental databases and face in the wild databases. The results reveal that our approach consistently outperforms state of the art methods for face alignment and tracking.
IEEE Transactions on Pattern Analysis and Machine Intelligence