基于人类注意机制的微表情检测方法

doi:10.3724/SP.J.1042.2022.02143

摘要/Abstract

摘要：

微表情是一种持续时间极短、不易被察觉的面部动作, 揭示了个体的真实情绪, 可以被广泛地应用于谎言识别等领域。而微表情检测的研究受到小样本问题的限制。针对该问题, 本文结合计算机视觉技术与认知心理学实验方法进行探索。首先, 结合眼动技术和呈现-判断范式与阈下情绪启动效应的行为实验范式, 考察微表情识别中选择注意分配的认知机制, 细化人类识别微表情时的特征兴趣区域。其次, 结合人类注意机制, 提出基于自监督学习的多模态微表情检测方法。通过理论和关键技术的突破, 为真实场景下微表情检测的应用奠定基础。

关键词: 微表情检测, 小样本问题, 人类注意机制, 自监督学习, 深度信息

Abstract:

Micro-expressions are facial movements that are extremely short and not easily perceived, often generated under high pressure. Micro-expressions can reveal the individual's hidden real emotions and are important non-verbal communication clues, widely used in lies detection and other fields. Due to the difficulty of eliciting, collecting, and labeling micro-expression samples, micro-expression-related research becomes a typical small-sample-size (SSS) problem. In order to enlighten the application of micro-expression analysis technology in complex real-life scenarios such as national security and clinical consultation, this study focuses on the SSS problem and proposes a micro-expression spotting method based on human attention mechanism with multi-branching self-supervised learning through the intersection of computer and psychology.

First, this study conducts an exploration related to attentional resources based on the cognitive mechanisms of psychological micro-expressions. A behavioral-experimental paradigm combining eye-movement techniques and a presentation-judgment paradigm with subthreshold emotion priming effects was used to examine the cognitive mechanisms of selective attention allocation in micro-expression recognition and to refine the distinct regions of interest in human recognition of micro-expressions. Thus, the model is effectively and directly enabled to acquire important micro-expression features from the input information. Then the relevant attention modules are further generated from multi-dimensions (time domain, spatial domain, and channel domain) by the deep learning network to improve the performance of the network in extracting micro-expression features with the limited sample size.

Second, this study proposes a multi-branching self-supervised learning method based on the human attention mechanism for micro-expression spotting. Training in many unlabeled video samples for the pre-text tasks enables the model to extract features from regions of interest of micro-expressions, including structural and detail features and video dynamic change patterns. Thus, the limitation caused by the SSS problem could be avoided.

Finally, the current data released for micro-expressions are video samples and do not include the corresponding depth information. This study will carry out a depth information-based micro-expression spotting method based on the first micro-expression database that includes image depth information being created by our research team. It enables self-supervised learning to learn the corresponding action patterns from the geometric information of the scene.

This research will achieve theoretical and technological breakthroughs in the field of automatic micro-expression spotting, improve the accuracy and reliability, and lay the foundation for the application of micro-expression spotting in realistic and complex scenarios.

Second, it can achieve the data augmentation of micro-expression samples by mining micro-expression clips in unlabeled videos. Thus, the micro-expression small sample problem could be solved, and the performance improvement of traditional supervised micro-expression spotting methods could be improved.

Key words: micro-expression spotting, small sample problem, human attention mechanism, self-supervised learning, depth information

中图分类号:

B842

李婧婷, 东子朝, 刘烨, 王甦菁, 庄东哲. (2022). 基于人类注意机制的微表情检测方法. 心理科学进展 , 30(10), 2143-2153.

LI Jingting, DONG Zizhao, LIU Ye, WANG Su-Jing, ZHUANG Dongzhe. (2022). Micro-expression spotting method based on human attention mechanism. Advances in Psychological Science, 30(10), 2143-2153.

图/表 8

图1 微表情样例：紧张情绪, 眉心处轻微向内收拢, 持续时间约300 ms。(资料来源：CASME II数据库, ?Xiaolan Fu)

图2 基于计算机的微表情研究趋势

图3 本研究提出的微表情检测方法与微表情小样本问题关系图

图4 基于小样本问题的微表情检测研究框架

图5 基于人类注意机制的多分支自监督学习微表情检测之技术框架

图6 结合心理学认知与计算机视觉的微表情注意力模块

图7 基于类魔方辅助任务的自监督网络框架

图8 结合人类注意机制的多分支自监督学习的微表情检测

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