A Hierarchical Probabilistic Model for Facial Feature Detection
This addresses the problem of robust facial feature detection for computer vision applications, but appears incremental as it builds on existing probabilistic modeling approaches.
The paper tackled facial feature detection under varying expressions and poses by proposing a hierarchical probabilistic model that infers true feature locations from image measurements, demonstrating effectiveness on benchmark databases.
Facial feature detection from facial images has attracted great attention 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. In this paper, we propose a hierarchical probabilistic model that could infer the true locations of facial features given the image measurements even if the face is with significant facial expression and pose. The hierarchical model implicitly captures the lower level shape variations of facial components using the mixture model. Furthermore, in the higher level, it also learns the joint relationship among facial components, the facial expression, and the pose information through automatic structure learning and parameter estimation of the probabilistic model. Experimental results on benchmark databases demonstrate the effectiveness of the proposed hierarchical probabilistic model.