Handling Data Imbalance in Automatic Facial Action Intensity Estimation

Abstract

Automatic Action Unit (AU) intensity estimation is a key problem in facial expression analysis. But limited research attention has been paid to the inherent class imbalance, which usually leads to suboptimal performance. To handle the imbalance, we propose (1) a novel multiclass under-sampling method and (2) its use in an ensemble. We compare our approach with state of the art sampling methods used for AU intensity estimation. Multiple datasets and widely varying performance measures are used in the literature, making direct comparison difficult. To address these shortcomings, we compare different performance measures for AU intensity estimation and evaluate our proposed approach on three publicly available datasets, with a comparison to state of the art methods along with a cross dataset evaluation.