A Survey Of Face Emotion Recognition Using Deep Learning Methods

The Deep learning techniques have significantly improved face emotion identification, a crucial component of human-computer interaction. This study examines facial emotion categorization with a variety of deep learning techniques, emphasizing well-known models such as VGG-19, ResNet-50, Inception-V3, and MobileNet. We investigate the effectiveness and constraints of these neural network systems through an analysis of different approaches for recognizing facial expressions. The pre-processing techniques, indicators of performance, and datasets used to assess these frameworks are all covered in the inquiry. This evaluation demonstrates the way The VGG model-19, ResNet-50, Inception-V3, and Mobile Network perform facial recognition of emotions tasks regarding precision, computational effectiveness, and real-time applications. The purpose of this study is to provide an in-depth review of the state-of-the-art and propose future lines of study for deep learning-driven face emotion detection development.