第二届面部微表情研讨会:多模态面部表情分析的先进技术

Jingting Li, Moi Hoon Yap, Wen-Huang Cheng, John See, Xiaopeng Hong, Xiabai Li, Su-Jing Wang
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引用次数: 1

摘要

微表情是指极短且不易察觉的面部动作,通常反映个人的真实情绪。微表情是理解人类真实情绪的重要线索,可用于非接触非感知欺骗检测或异常情绪识别。在国家安全、司法实践、卫生预防、临床实践等方面具有广阔的应用前景。然而,由于微表情具有持续时间短、强度低、局部不对称等特点,对微表情特征的提取和学习具有很大的挑战性。此外,结合深度学习技术的智能微表情分析也受到小样本问题的困扰。不仅微表情提取非常困难,微表情注释也非常耗时费力。更重要的是,微表情的生成机制尚不明确,制约了微表情在真实场景中的应用。FME'22是这一研究领域的首次研讨会,旨在促进来自这一研究领域的研究人员和学者之间的互动,也包括来自更广泛的、一般的表达和心理学研究领域的研究人员和学者。完整的FME'22研讨会记录可在:https://dl.acm.org/doi/proceedings/10.1145/3552465。
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FME '22: 2nd Workshop on Facial Micro-Expression: Advanced Techniques for Multi-Modal Facial Expression Analysis
Micro-expressions are facial movements that are extremely short and not easily detected, which often reflect the genuine emotions of individuals. Micro-expressions are important cues for understanding real human emotions and can be used for non-contact non-perceptual deception detection, or abnormal emotion recognition. It has broad application prospects in national security, judicial practice, health prevention, clinical practice, etc. However, micro-expression feature extraction and learning are highly challenging because micro-expressions have the characteristics of short duration, low intensity, and local asymmetry. In addition, the intelligent micro-expression analysis combined with deep learning technology is also plagued by the problem of small samples. Not only is micro-expression elicitation very difficult, micro-expression annotation is also very time-consuming and laborious. More importantly, the micro-expression generation mechanism is not yet clear, which shackles the application of micro-expressions in real scenarios. FME'22 is the inaugural workshop in this area of research, with the aim of promoting interactions between researchers and scholars from within this niche area of research and also including those from broader, general areas of expression and psychology research. The complete FME'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3552465.
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