Department of Electronics and Communication Engineering, Intelligent Signal Processing (ISP) Research Lab, Kuwait College of Science and Technology (A Private University), Al-Jahra, Kuwait.
Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Kumait, Kuwait.
PLoS One. 2021 Feb 18;16(2):e0247131. doi: 10.1371/journal.pone.0247131. eCollection 2021.
Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangulation method for extracting a novel set of geometric features is proposed to classify six emotional expressions (sadness, anger, fear, surprise, disgust, and happiness) using computer-generated markers. The subject's face is recognized by using Haar-like features. A mathematical model has been applied to positions of eight virtual markers in a defined location on the subject's face in an automated way. Five triangles are formed by manipulating eight markers' positions as an edge of each triangle. Later, these eight markers are uninterruptedly tracked by Lucas- Kanade optical flow algorithm while subjects' articulating facial expressions. The movement of the markers during facial expression directly changes the property of each triangle. The area of the triangle (AoT), Inscribed circle circumference (ICC), and the Inscribed circle area of a triangle (ICAT) are extracted as features to classify the facial emotions. These features are used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17% using a Random Forest (RF) classifier compared to other features and classifiers in distinguishing emotional expressions.
情绪在人际交流中起着重要作用，也能改善社交生活。近年来，面部表情识别在开发人机界面（HCI）和人形机器人方面得到了广泛应用。在这项工作中，提出了一种三角测量方法，用于提取一组新的几何特征，使用计算机生成的标记来对六种情感表达（悲伤、愤怒、恐惧、惊讶、厌恶和快乐）进行分类。使用 Haar 特征识别主体的面部。应用数学模型以自动方式在主体面部上定义的位置处定位八个虚拟标记的位置。通过操纵八个标记的位置形成五个三角形，每个三角形的一个边缘。之后，在受试者表达面部表情时，Lucas-Kanade 光流算法会不间断地跟踪这些标记。标记在面部表情期间的运动直接改变每个三角形的属性。三角形的面积（AoT）、内接圆周长（ICC）和三角形的内接圆面积（ICAT）被提取为特征，用于使用各种类型的机器学习算法对面部表情进行分类。使用随机森林（RF）分类器，与其他特征和分类器相比，三角形的内接圆面积（ICAT）特征在区分情感表达方面给出了 98.17%的最大平均分类率。
