Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition

TL;DR

This paper introduces a Target Sample Re-Generator (TSRG) to improve cross-database micro-expression recognition by aligning feature distributions between different datasets, leading to better classification accuracy.

Contribution

The paper proposes TSRG, a novel method that re-generates target samples to match source feature distributions, enhancing cross-database micro-expression recognition performance.

Findings

TSRG outperforms recent state-of-the-art methods.

Extensive experiments on SMIC and CASME II validate effectiveness.

Re-generated samples improve classifier accuracy.

Abstract

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.