Exploring the Impact of Convolutional Neural Networks on Facial Emotion Detection and Recognition

Abstract

Emotional analytics is a fascinating blend of psychology and technology, with one of the primary methods for recognizing emotions involving facial expression analysis. Facial emotion detection has advanced significantly, utilizing deep learning algorithms to identify common emotions. In recent years, substantial progress has been made in automatic facial emotion recognition (FER). This technology has been applied across various industries to enhance interactions between humans and machines, particularly in human-centered computing and the emerging field of emotional artificial intelligence (EAI). Researchers focus on improving systems’ capabilities to recognize and interpret human facial expressions and behaviors in diverse contexts. The impact of convolutional neural networks (CNNs) on this field has been profound, as these networks have undergone significant development, leading to diverse architectures designed to address increasingly complex challenges. This article explores the latest advancements in automated emotion recognition using computational intelligence, emphasizing how contemporary deep learning models contribute to the field. It provides a review of recent developments in CNN architectures for FER over the past decade, demonstrating how deep learning-based methods and specialized databases collaborate to achieve highly accurate outcomes.