Face Features Tracking with Conditional Active Appearance Model

An improved active appearance model(AAM) based on inverse compositional algorithm, called conditional active appearance model(CAAM), is proposed to complete the facial key feature points localization and tracking accurately.First, the mapping between the scattered correspondence and the structured correspondence is established via kernel ridge regression.Assuming the annotation of frontal face image is known, the feature points of profile face images are automatically initialized from the frontal image based on the mapping.Second, the shape model and the appearance model between the frontal face and arbitrary profile face are established with conditional active appearance model, and the model parameters are optimized iteratively through inverse compositional algorithm.Finally, face features tracking is completed by achieving shape contour with optimum conditional model parameters.It is proved by experiments that the method demonstrates good performance in face tracking.Higher accuracy in positioning and less computing time can be obtained compared with other related methods.