A Comparative Study on Deep Facial Expression Recognition

This paper reviews state-of-the-art developments in facial emotion identification with the help of Deep learning technology. This study has reviewed the recent research developments in facial emotion recognition, along with the performance of leading deep learning architectures and algorithms. Further, this study discusses about the contribution, model performance, and limitations of various architectures like Convolutional Neural Network (CNNs) and Recurrent Neural Networks (RNNs). Moreover, this study has examined the effectiveness of various cutting-edge deep learning algorithms for detecting facial expressions. As a result, it has been found that the most successful architectures are Hybrid CNN-RNNs, which combine convolutional layers with recurrent ones. This is due to their ability to learn hierarchical representations of data and exploit temporal dependencies in inputs. There are also architectures that use meta-parameters such as attention vectors or word embeddings; however, they do not provide significant improvements over RNNs alone. Also, this study covered the application of deep learning to facial emotion recognition, as well as its potential limits. Next, this study discusses about some research works that can be done in this field. This research study reviews the literature on face recognition. It also explains the relationship between facial emotion and facial feature extraction, which is essential for emotion recognition.