A VGG16 Based Hybrid Deep Convolutional Neural Network Based Real-Time Video Frame Emotion Detection System for Affective Human Computer Interaction

Abstract

Abstract: Facial emotion recognition is field of computer vision that deals with recognizing the emotions of the user by facial expression analysis. The development of facial emotion recognition technology has the potential to enhance human-computer interaction by making machines more responsive to human emotions. For recognizing facial emotions, a variety of machine learning methods have been used, such as deep neural networks, support vector machines, decision trees, and among others, Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their combinations have all been suggested as designs, which have proven especially effective in this field. However, despite substantial advancements in the area, there are still difficulties in attaining accurate and consistent facial expression identification in real-time everyday situations. The effectiveness and usability of face expression detection systems for diverse applications must therefore be improved and efficient systems be developed to expand the focus on further applications of emotions in achieving affective Human computer interaction. My main objective in this paper will be to focus on being able to accurately identify user’s emotion in a real world scenario. I will be a designing a hybrid deep learning CNN architectures for the real world implementation of the real-time emotion recognition system for improving Human Computer Interaction