Emotion recognition is a basic communication tool in our daily interaction, and the recognition of emotions without contact and with high sensitivity may be very useful for various purposes. This paper presents a preliminary experimental investigation in which emotions of healthy subjects were recognized while they performed facial gestures related to those different emotions. Their faces were illuminated with a few laser spots and the formed back-scattered speckle patterns were analyzed with a camera having proper optics. By analyzing the temporal variation in the spatial distribution of those speckle patterns we estimated the muscles’ contraction–release motion in specific locations. The used data amount for the estimation procedure was less than 1% of the face frame so as to maintain the subjects’ privacy. Moreover, the presented optic method enables the detection of minor movements that cannot be recognized by the naked eye or conventional visual processing. After applying the machine learning k-nearest neighbors algorithm, we succeeded in reaching 89% accuracy in the recognition of emotions for the combination of two classification steps: subject recognition among the participants, and then emotion recognition among three optional emotions: happiness, sadness and neutral expression.

情感识别是我们日常互动中的基本沟通工具，无需接触且具有高度敏感性的情感识别对于各种目的可能非常有用。本文提出了一项初步的实验研究，其中健康受试者在执行与这些不同情绪相关的面部手势时的情绪得以识别。用少量激光点照亮他们的脸，并用具有适当光学特性的相机分析形成的后向散射斑点图案。通过分析这些斑点图案的空间分布的时间变化，我们估计了特定位置的肌肉收缩释放运动。估计程序所使用的数据量少于面部框架的1％，以保持受试者的隐私。而且，提出的光学方法能够检测肉眼或常规视觉处理无法识别的微小运动。应用机器学习后k-最近邻算法，通过两个分类步骤的组合，我们成功地达到了89％的情感识别准确度：参与者之间的主题识别，然后是幸福，悲伤和中立表达三个可选情感之间的情感识别。