Machine Learning Techniques for Emotion Detection Using Eye Gaze Localisation

Abstract

The ability to detect and interpret human emotions is vital for effective communication. This chapter explores the integration of machine learning with eye gaze localization for emotion detection, offering a non-intrusive and natural means of expression. Eye gaze data, encompassing parameters like gaze direction and pupil dilation, provides a rich basis for machine learning models. The chapter emphasizes the significance of quality training data, delving into data collection, pre-processing, and feature extraction. Various machine learning models, including support vector machines and deep learning models like CNNs and RNNs, are discussed for emotion detection. Evaluation metrics and cross-validation techniques ensure model accuracy. Practical applications in healthcare, marketing, and human-computer interaction are presented, showcasing the benefits. Despite successes, challenges like data bias and privacy concerns persist. The chapter encourages ongoing exploration of emerging technologies and sensory data integration for more robust models in the evolving field of emotion detection using eye gaze localization.