Facial Action Unit Detection Based on Teacher-Student Learning Framework for Partially Occluded Facial Images

Abstract

Facial action unit (AU) detection is an important task in facial expression analysis. However, occlusion is a major hindrance in practical applications of AU detection as it interferes with extracting features from facial images and makes it difficult to capture the occurrence of AUs. To address this problem, we first construct a database for AU detection with occlusion by synthesizing occlusion objects such as bangs (i.e., fringe), glasses, and hands on facial images. Then we apply a teacher-student learning framework with two types of loss functions to AU detection for occluded facial images. To improve our model's robustness to occlusion, we propose a loss function for order regularization which considers the relationship between facial images as well as conventional distillation loss. The results of our experiment with our database for occluded images demonstrate that our method is effective for detecting AUs with occluded facial images.