The recognition of human emotions is an essential part of interpersonal communication that can help improve various fields such as healthcare, social robotics, and marketing research. One of the promising techniques for this recognition is facial emotion recognition which involves the analysis of facial expressions to identify and understand human emotions. Convolutional neural networks (CNNs) are a specialized type of artificial neural networks that can efficiently process images and are used for facial emotion recognition. This paper aims to present an overview of the use of CNNs for facial emotion recognition. It discusses the basic concepts of CNNs, their architecture, and how they can be trained on large datasets of facial images to predict emotions. The paper also emphasizes the importance of feature extraction in accurately classifying emotions. Finally, the paper concludes by discussing the potential applications of facial emotion recognition using CNNs in different fields and ethical and social implications of this technology. It emphasizes the need for transparency and accountability in the development and deployment of this technology. Overall, this paper provides a comprehensive overview of the use of CNNs for facial emotion recognition and its potential applications in various fields.

Deep Learning, Neural Network, Convolution Neural Network (CNN), facial emotion recognition, Artificial Neural Networks.