AI-Powered Emotional Recognition in Human Thermal Images

Facial expressions convey non-verbal information between humans in face-toface interactions. Automatic facial expression recognition, which plays a vital role in human-machine interfaces, has attracted increasing attention from researchers since the early nineties. Classical machine learning approaches often require a complex feature extraction process and produce poor results. In this paper, we apply recent advances in deep learning to propose effective deep Convolutional Neural Networks (CNNs) that can accurately interpret semantic information available in faces in an automated manner without hand-designing of features descriptors. We also apply different loss functions and training tricks in order to learn CNNs with a strong classification power. The study aims to analyze facial expressions associated with various emotions, including happiness, sadness, anger, fear, surprise, and disgust.The CNN algorithm is utilized as a powerful classifier to facilitate the extraction and selection of crucial features, allowing for a robust and accurate identification of various emotional states. The integration of thermal imaging and AI-based emotion recognition holds promise for applications in diverse fields, including psychology, healthcare, and human-computer interaction. This research contributes to the advancement of emotion analysis techniques by leveraging state-of-the-art AI technologies to decode complex emotional expressions through thermal facial data.