A Hierarchical Probabilistic Model for Facial Feature Detection

TL;DR

This paper introduces a hierarchical probabilistic model that improves facial feature detection by capturing shape variations and relationships among facial components, expressions, and poses, even under challenging conditions.

Contribution

It presents a novel hierarchical probabilistic framework that automatically learns facial component relationships and shape variations for robust feature detection.

Findings

Effective on benchmark databases

Handles significant facial expressions and poses

Outperforms existing methods in accuracy

Abstract

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.