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facial action unit fusion

Facial action unit fusion is a computational technique in computer vision and affective computing that combines spatial, temporal, or feature-level information from individual facial muscle movements to recognize overall facial expressions and subtle micro-expressions. Based on the Facial Action Coding System, which categorizes human facial behavior into discrete, localized muscular motions known as action units, this process integrates data from multiple distinct facial regions or detection channels into a unified representation. By modeling the interactions and co-occurrences among different muscle groups rather than evaluating the face solely as an undifferentiated whole, facial action unit fusion reduces background interference and enhances the sensitivity of automated systems to rapid, fine-grained emotional signals.

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Micron-BERT: BERT-Based Facial Micro-Expression Recognition

Micron-BERT: BERT-Based Facial Micro-Expression Recognition

Xuan-Bac Nguyen, Chi Nhan Duong, Xin Li, Susan Gauch, Han-Seok Seo, Khoa Luu

OrganizationsConcordia UniversityUniversity of ArkansasWest Virginia University

Why you should read this

Proposes a self-supervised BERT-based framework that captures subtle facial micro-expressions between video frames without manual annotations by pairing diagonal micro-attention with an unsupervised patch-of-interest detector.

Micro-expression recognition is one of the most challenging topics in affective computing. It aims to recognize tiny facial movements difficult for humans to perceive in a brief period, i.e., 0.25 to 0.5 seconds. Recent advances in pre-training deep Bidirectional Transformers (BERT) have significantly improved self-supervised learning tasks in computer vision. However, the standard BERT in vision problems is designed to learn only from full images or videos, and the architecture cannot accurately detect details of facial micro-expressions. This paper presents Micron-BERT (µ-BERT), a novel approach to facial micro-expression recognition. The proposed method can automatically capture these movements in an unsupervised manner based on two key ideas. First, we employ Diagonal Micro-Attention (DMA) to detect tiny differences between two frames. Second, we introduce a new Patch of Interest (PoI) module to localize and highlight micro-expression interest regions and simultaneously reduce noisy backgrounds and distractions. By incorporating these components into an end-to-end deep network, the proposed µ-BERT significantly outperforms all previous work in various micro-expression tasks. µ-BERT can be trained on a large-scale unlabeled dataset, i.e., up to 8 million images, and achieves high accuracy on new unseen facial micro-expression datasets. Empirical experiments show µ-BERT consistently outperforms state-of-the-art performance on four micro-expression benchmarks, including SAMM, CASME II, SMIC, and CASME3, by significant margins. Code will be available at https://github.com/uark-cviu/Micron-BERT

Added

2026-09-26