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

arXiv: https://arxiv.org/abs/2111.15361 · HTML: https://mlsanity.com/paper/2111.15361/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild](https://mlsanity.com/paper/2008.05924/) (2020)
- [Cross-Database Micro-Expression Recognition: A Benchmark](https://mlsanity.com/paper/1812.07742/) (2018)
- [SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition](https://mlsanity.com/paper/2010.09342/) (2020)
- [Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition](https://mlsanity.com/paper/1707.08645/) (2017)

## Built on by (most cited first, 2 of 2)

- [Multi-scale multi-modal micro-expression recognition algorithm based on transformer](https://mlsanity.com/paper/2301.02969/) (2023)
- [MPT: Motion Prompt Tuning for Micro-Expression Recognition](https://mlsanity.com/paper/2508.09446/) (2025)

## Abstract (arXiv)

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.