Enhancing Facial Emotion Level Recognition: A CNN-Based Approach to Balancing Data
摘要
Facial recognition plays an important role in human–computer interaction and many other artificial intelligence technologies. This work presents a new way to ensure the accuracy and composition of facial expressions with the help of convolutional neural networks (CNN). Research involves improving and expanding existing data and updating ideas to improve and iterate the model. This research contributes to the advancement of facial recognition by highlighting the importance of data curation and optimization in the development of performance models. The CNN model developed in this study is a good tool for measuring the accuracy and quantity of facial features and can be used in human–computer, technical awareness, and mental health services.