Enhancing Eye Emotion Recognition with the Haar Classifier Using Co-Evolutionary Hybrid Intelligence

Abstract

This work explores the challenging task of identifying emotions from the eyes through the analysis of nonverbal cues and facial expressions. The study investigates the various emotions that the eyes can convey and their significance in emotional expression, including happiness, sadness, anger, fear, surprise, and contempt. The practical applications of this research include the potential use of computer vision algorithms to analyze images or videos of the eye region to identify emotions. The coevolution theory suggests that humans and robots can collaborate to achieve a shared goal. Therefore, this study aims to develop algorithms that can identify variations in pupil size, eye movements, and other traits linked to various emotional states, such as arousal, surprise, and disgust. The results of the research show that these algorithms can accurately identify emotions from the eyes, and a human expert can validate their interpretations. The methods used in this study include analyzing facial expressions and nonverbal cues, developing computer vision algorithms, and working with human experts to validate the results. The conclusions drawn from this work suggest that emotional identification from the eyes is a promising area of research that has practical applications in various fields, such as robotics, psychology, and human-computer interaction.

Keywords

• emotion recognition
• eye tracking
• real-time detection
• computer vision
• machine learning