Face Emotion Recognition (FER) Using Convolutional Neural Network (CNN) in Machine Learning

Abstract

Abstract: Facial Emotion Recognition (FER) is a burgeoning field within the realm of machine learning, central to computer vision and artificial intelligence. This paper offers a detailed examination of the role of Convolutional Neural Networks (CNNs) in advancing FER methodologies. Focusing on the utilization of facial images as a primary information source, the review delves into traditional FER approaches, categorizing and summarizing foundational systems and algorithms. In response to the evolving landscape, this study specifically explores the integration of CNNs in FER strategies. CNNs have emerged as pivotal tools for capturing intricate spatial features inherent in facial expressions, demonstrating their effectiveness in enhancing the nuanced interpretation of emotional states. The discussion emphasizes the adaptability and robustness of CNNs in addressing the complexities of facial emotion recognition. This paper provides insights into publicly accessible evaluation metrics and benchmark results, establishing a standardized framework for the quantitative assessment of FER research employing CNNs. Aimed at both newcomers and seasoned researchers in the FER domain, this review serves as a comprehensive guide, imparting foundational knowledge and steering future investigations. The ultimate goal is to contribute to a deeper understanding of the latest state-of-the-art studies in facial emotion recognition, particularly within the context of CNNs in machine learning.