Local binary pattern probability model based facial feature localization

In this paper, an active shape model (ASM) based facial feature localization strategy is proposed, which employs a local binary pattern (LBP) probability model. Due to the computation simplicity and illumination insensitivity of LBP texture descriptor and the learning ability of the probability model, the algorithm is robust and fast. In addition, component-based ASM is used to impose reasonable constraints on the shape. Multi-state shape and texture models with state classifier are trained to handle highly flexible components, i.e. eyes and mouth. Our database consisting of tens of persons with various expressions and illuminations is used to train and verify the proposed algorithm. The experiments demonstrate its accuracy, efficiency and robustness.