FaceSleuth-R: Adaptive Orientation-Aware Attention for Robust Micro-Expression Recognition

• URL: http://arxiv.org/abs/2506.02695v3
• Date: Sat, 08 Nov 2025 07:12:23 GMT
• Title: FaceSleuth-R: Adaptive Orientation-Aware Attention for Robust Micro-Expression Recognition
• Authors: Linquan Wu, Tianxiang Jiang, Haoyu Yang, Wenhao Duan, Shaochao Lin, Zixuan Wang, Yini Fang, Jacky Keung,
• Abstract summary: We introduce textbfFaceSleuth-R, a framework centered on our novel textbfSingle-Orientation Attention (SOA) module.<n> SOA is a lightweight, differentiable operator that enables the network to learn layer-specific optimal orientations.<n>We demonstrate that SOA consistently discovers a universal near-vertical motion prior across diverse datasets.
• Score: 25.77351512351652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Micro-expression recognition (MER) has achieved impressive accuracy in controlled laboratory settings. However, its real-world applicability faces a significant generalization cliff, severely hindering practical deployment due to poor performance on unseen data and susceptibility to domain shifts. Existing attention mechanisms often overfit to dataset-specific appearance cues or rely on fixed spatial priors, making them fragile in diverse environments. We posit that robust MER requires focusing on quasi-invariant motion orientations inherent to micro-expressions, rather than superficial pixel-level features. To this end, we introduce \textbf{FaceSleuth-R}, a framework centered on our novel \textbf{Single-Orientation Attention (SOA)} module. SOA is a lightweight, differentiable operator that enables the network to learn layer-specific optimal orientations, effectively guiding attention towards these robust motion cues. Through extensive experiments, we demonstrate that SOA consistently discovers a universal near-vertical motion prior across diverse datasets. More critically, FaceSleuth-R showcases superior generalization in rigorous Leave-One-Dataset-Out (LODO) protocols, significantly outperforming baselines and state-of-the-art methods when confronted with domain shifts. Furthermore, our approach establishes \textbf{state-of-the-art results} across several benchmarks. This work highlights adaptive orientation-aware attention as a key paradigm for developing truly generalized and high-performing MER systems.

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