Abstract
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.
Highlights
Emotions play a crucial role in interpersonal communication between humans; and humans and machines
We propose a fully automated facial emotional expression classification system using novel geometric features
We prove that the representation based on triangle features outperform conventional image-based facial expression recognition with lesser computational complexity
Summary
Emotions play a crucial role in interpersonal communication between humans; and humans and machines It becomes the most popular field of research in the last decade to develop intelligent humanoid robots and human-machine interface (HMI) systems. Over the last few decades, several algorithms have been proposed by different researchers in detecting facial expressions. The present work aims to develop an intelligent and more reliable online facial emotion recognition system using geometrical facial features extracted through the triangulation approach. Facial expression recognition algorithms play a significant role in developing intelligent systems over the last few decades [13,14,15,16]. The experimental results of the proposed method outperform the most state-of-the-art facial emotional expression recognition systems.
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