Abstract
Facial micro-expressions, characterized by their subtle and brief nature, are valuable indicators of genuine emotions. Despite their significance in psychology, security, and behavioral analysis, micro-expression recognition remains challenging due to the difficulty of capturing subtle facial movements. Optical flow has been widely employed as an input modality for this task due to its effectiveness. However, most existing methods compute optical flow only between the onset and apex frames, thereby overlooking essential motion information in the apex-to-offset phase. To address this limitation, we first introduce a comprehensive motion representation, termed Magnitude-Modulated Combined Optical Flow (MM-COF), which integrates motion dynamics from both micro-expression phases into a unified descriptor suitable for direct use in recognition networks. Building upon this principle, we then propose FMANet, a novel end-to-end neural network architecture that internalizes the dual-phase analysis and magnitude modulation into learnable modules. This allows the network to adaptively fuse motion cues and focus on salient facial regions for classification. Experimental evaluations on the MMEW, SMIC, CASME-II, and SAMM datasets, widely recognized as standard benchmarks, demonstrate that our proposed MM-COF representation and FMANet outperforms existing methods, underscoring the potential of a learnable, dual-phase framework in advancing micro-expression recognition.
Abstract (translated)
面部微表情由于其微妙和短暂的特性，是真实情感的重要指标。尽管在心理学、安全和行为分析领域具有重要意义，但由于捕捉细微面部动作的困难性，微表情识别仍然极具挑战性。光学流因其有效性而广泛用于此类任务作为输入模式。然而，大多数现有的方法仅计算从起始帧到峰值帧的光学流，从而忽略了顶峰至结束阶段的重要运动信息。 为了解决这一局限性，我们首先引入了一种全面的运动表示方式——调制幅度组合光学流（Magnitude-Modulated Combined Optical Flow, MM-COF），它将微表情两个相位中的运动动态集成到一个统一描述符中，可以直接用于识别网络。在此基础上，我们进一步提出FMANet，这是一种新的端到端神经网络架构，内部化了双相分析和幅度调制为可学习模块。这使网络能够自适应地融合运动线索并关注于面部显著区域以进行分类。 在MMEW、SMIC、CASME-II和SAMM等广泛认可的标准数据集上的实验评估表明，我们提出的MM-COF表示法和FMANet优于现有方法，突显了可学习双相框架在推进微表情识别方面的潜力。
URL
https://arxiv.org/abs/2510.07810
