Enhancing Real-time Facial Sentiment Recognition Systems through Deep Learning Architectures

Facial Emotion Recognition (FER) has emerged as a pivotal domain within computer vision, with applications spanning diverse fields such as human-computer interaction, mental health monitoring, and security systems. While significant strides have been made in recent years, accurate recognition of nuanced emotions, particularly fear, remains a challenging task. This research contributes to the advancement of FER technology, paving the way for more sophisticated and empathetic human-computer interactions. By discussing about the reliable and efficient methods for real-time fear detection, this study enables the potential to revolutionize various applications, including mental health monitoring, security surveillance, and autonomous vehicles.