Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition

TL;DR

This paper introduces TGSR, a transfer group sparse regression method that enhances cross-database micro-expression recognition by selecting salient facial regions, reducing domain gaps, and improving recognition accuracy.

Contribution

The paper proposes a novel TGSR method that effectively selects salient facial regions and alleviates domain differences for better cross-database micro-expression recognition.

Findings

TGSR outperforms most state-of-the-art methods on CASME II and SMIC datasets.

Salient facial regions learned by TGSR are discriminative and interpretable.

The method reduces computational cost while improving recognition performance.

Abstract

Cross-Database Micro-Expression Recognition (CDMER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize the Micro-Expressions (MEs) of different subjects captured by different imaging devices in different scenes. The development of CDMER is faced with two key problems: 1) the severe feature distribution gap between the source and target databases; 2) the feature representation bottleneck of ME such local and subtle facial expressions. To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i.e., the salient facial region selection. Compared with previous transfer group sparse methods, our proposed TGSR has the ability to select the salient facial regions, which is effective in alleviating the aforementioned problems for better performance and reducing the computational cost at the same time. We use two public ME databases, i.e., CASME II and SMIC, to evaluate our proposed TGSR method. Experimental results show that our proposed TGSR learns the discriminative and explicable regions, and outperforms most state-of-the-art subspace-learning-based domain-adaptive methods for CDMER.