Affective Computing Models: from Facial Expression to Mind-Reading ("ACMod")

人类表现出广泛的情感和认知状态，并与之交流。读心术使人类能够预测、建模和解释彼此的行为，超出了其他动物的能力。尽管最近的研究表明，类人猿成功地完成了错误信念的任务，但可以说这一说法是可以得出的。因此，读心术是人类社会交往和交流的基础。在读心术中，最重要的标志之一是面部表情，因为它传达的是反映精神状态的关键信息，当人们感知他人的感受和态度时，它与55%的信息有关。自从Duchenne5在1862年研究了单个面部肌肉的电刺激，十年后，达尔文发表了《人类和动物的情绪表达》，为面部表情的共同祖先提供了理由。关于面部表情的研究已经引起了心理学、神经科学和计算机科学等不同学科的广泛关注。近年来，计算技术的发展和海量的在线人脸图像/视频使得基于深度学习的面部表情识别(FER)的发展成为可能。到目前为止，自动表情识别已经取得了很好的进展，从静态图像到动态视频分析，从动作/姿势到自发表情，从宏观表情到微观表情。概括地说，日益增长的挑战包括：1)大量的心理学著作支持使用情绪评估理论通过面部行为来检测内在情绪。同时，计算机科学的研究主要集中在外表或几何面部模型上，而忽视了潜在的生物驱动机制：2)来自不同文化的可用数据有限，阻碍了机器学习方法的发展；3)微表情，快速(1/25-1/3秒)，微妙，难以通过意志力控制的面部表情，没有被研究文化不一致；

Humans exhibit and communicate with a wide range of affective and cognitive states. Mind reading allows humans to predict, model, and interpret each other's behaviour beyond the capabilities of other animals, a claim that arguably can be made despite recent research suggesting apes being successful with false-belief tasks. Therefore, mind reading is fundamental to human social interaction and communication. In mind reading, one of the most important signs is facial expression, as it conveys critical information that reflects mental states and relates to 55% of information when people perceive others' feelings and attitudes. Since Duchenne5 studied the electro-stimulation of individual facial muscles in 1862 and ten years later, Darwin published "The Expression of the Emotions in Man and Animals", making a case for shared ancestry of facial expressions. Research on facial expressions has attracted a lot of attention from different disciplines such as psychology, neuroscience and computer science. In recent years, the development of computing technologies and massive online facial images/videos enabled the boosting of deep learning-based facial expression recognition (FER). To date, automatic FER has achieved excellent progress, from static image to dynamic video analysis, from acted/posed to spontaneous expressions, from macro-expressions to micro-expressions.In summary, the rising challenges include, 1) Substantial psychological works support the use of appraisal theories of emotion for internal emotion detection through facial behaviours. At the same time, research in computer science mainly focuses on appearance or geometric facial modelling but ignores the underlying biologically-driven mechanism; 2) There is limited available data from different cultures, hindering the research on machine learning method development; 3) Micro-expressions, rapid (1/25 to 1/3 second), subtle, and involuntary facial expressions that are difficult to control through one's willpower, is not studied for culture inconsistency;