Paper Title
Enhancing Facial Emotion Recognition with Advanced Convolution Neural Networks

Abstract
Facial Emotion Recognition (FER) is a significant field in computer vision and artificial intelligence. The ability to accurately detect and interpret human emotions through facial expressions has wide-ranging applications, from human-computer interaction to healthcare. While traditional methods relied on handcrafted features, deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized the field. CNNs excel at automatically learning hierarchical feature representations from raw pixel data, making them highly effective for FER. However, challenges such as data scarcity, imbalanced datasets, and real-world variations in facial expressions remain. To address these challenges, techniques like data augmentation, class weighting, and transfer learning are employed. Additionally, advancements in attention mechanisms, multi-task learning, and generative adversarial networks (GANs) have shown promise in improving model robustness and generalizability. Keywords - Facial Emotion Recognition, Convolutional Neural Networks, Generative Adversarial Networks, Data augmentation