Micron-BERT: BERT-based Facial Micro-Expression Recognition
Xuan-Bac Nguyen, Chi Nhan Duong, Xin Li, Susan Gauch, Han-Seok Seo,, Khoa Luu

TL;DR
Micron-BERT introduces a specialized BERT-based model with novel attention and localization modules to effectively recognize tiny facial micro-expressions, significantly outperforming previous methods in accuracy and scalability.
Contribution
The paper proposes Micron-BERT, a new architecture with Diagonal Micro-Attention and Patch of Interest modules for improved micro-expression recognition, trained on large unlabeled datasets.
Findings
Outperforms state-of-the-art on four benchmarks
Effective on large-scale unlabeled datasets
Accurately detects subtle facial micro-movements
Abstract
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 (-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…
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Taxonomy
TopicsEmotion and Mood Recognition · Advanced Computing and Algorithms · Face and Expression Recognition
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Multi-Head Attention · Attention Is All You Need · Attention Dropout · Weight Decay · Adam · Softmax · Linear Layer · WordPiece · Residual Connection
