Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
This addresses the problem of facial feature tracking for computer vision applications, but it is incremental as it builds on existing RBM methods.
The paper tackled robust facial feature tracking under varying expressions and poses by proposing a face shape prior model based on Restricted Boltzmann Machines and Deep Belief Networks, achieving robust and accurate tracking on benchmark databases.
Facial feature tracking is an active area in computer vision due to its relevance to many applications. It is a nontrivial task, since faces may have varying facial expressions, poses or occlusions. In this paper, we address this problem by proposing a face shape prior model that is constructed based on the Restricted Boltzmann Machines (RBM) and their variants. Specifically, we first construct a model based on Deep Belief Networks to capture the face shape variations due to varying facial expressions for near-frontal view. To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes. Finally, we introduce methods to systematically combine the face shape prior models with image measurements of facial feature points. Experiments on benchmark databases show that with the proposed method, facial feature points can be tracked robustly and accurately even if faces have significant facial expressions and poses.