关键词: 脸部特征检测, 分层概率模型, 局部形态, 约束, 检测误差

Abstract: Facial feature detection problem is a hot research topic in the field of computer vision. It is a nontrivial task since the appearance and shape of the face tend to change under different conditions. Aiming at the deficiency of the existing facial feature detection algorithms, a hierarchical probabilistic model is proposed that can infer the true facial feature locations given image measurements. Firstly, it implicitly models the local shape variation for each facial component. Secondly, it learns the joint relationship among facial components, the facial expression and the pose in the higher level by searching the optimal structure and parameterizations of the model. Finally, the true facial feature locations are inferred through the bottom-up lower level shape constraints of facial components and the top-down constraint from the relationship among facial components. Experimental results on benchmark databases demonstrate that, the detection performance of the proposed method is obviously better than that of the most recent face feature detection algorithm.

Key words: facial feature detection, hierarchical probabilistic model, local shape, constraints, detection error