Published Paper Details:

Facial expression recognition is a crucial area of study in the field of computer vision. Research on nonverbal communication has shown that a significant amount of deliberate information is sent via facial expressions. Facial expression recognition is a crucial field in computer vision that deals with the significant impact of nonverbal communication. Expression recognition has lately been extensively used in the medical and advertising sectors. Facial emotion recognition is a technique that examines facial expressions in static images and videos to uncover information about an individual's emotional state. Facial expression recognition (FER) is a vital area of study within computer vision due to its significant impact on nonverbal communication. This review paper synthesizes recent advancements in FER, focusing on the integration of deep learning techniques and generative models to improve accuracy and robustness in diverse settings. Key contributions include enhancements in handling facial feature variability, occlusions, and lighting conditions. Notable methods such as Generative Adversarial Networks (GANs) and convolutional neural networks (CNNs) are explored for their effectiveness in creating and recognizing facial expressions. The research underscores the importance of both local and global facial features, demonstrating that a hybrid approach often yields superior results. Additionally, the paper discusses the application of FER in fields such as healthcare, human-computer interaction, and psychology, highlighting the broad implications of improved FER technology. By examining datasets, methodologies, and outcomes from various studies, this review identifies current trends and challenges, proposing directions for future research to enhance the understanding and interpretation of human emotions through facial expressions.