Social Robot Face Emotion Recognition Using Wavelet Transformation

Abstract

Facial emotion recognition plays a pivotal role in enabling social robots to interact with humans effectively, as it allows robots to perceive and respond to human emotional states. Emotions are complex and dynamic, involving various physiological and behavioral cues. This study explores the use of Wavelet Transform as a tool for enhancing emotion recognition in social robots. The Wavelet Transform is a mathematical technique that captures both time and frequency-domain information simultaneously, making it ideal for analyzing physiological signals like EEG and ECG. The findings show that Wavelet Transform-based emotion recognition can significantly improve a social robot's ability to perceive and respond to human emotions in real time. This advancement has the potential to revolutionize human-robot interactions in various domains, such as healthcare, education, and entertainment, by enabling more empathetic and context-aware interactions. This research contributes to the ongoing development of emotionally intelligent social robots, paving the way for more natural and meaningful human-robot interactions