Video-based emotion sensing and recognition using convolutional neural network based kinetic gas molecule optimization

Abstract

Human facial expressions are thought to be important in interpreting one's emotions. Emotional recognition plays a very important part in the more exact inspection of human feelings and interior thoughts. Over the last several years, emotion identification utilizing pictures, videos, or voice as input has been a popular issue in the field of study. Recently, most emotional recognition research focuses on the extraction of representative modality characteristics and the definition of dynamic interactions between multiple modalities. Deep learning methods have opened the way for the development of artificial intelligence products, and the suggested system employs a convolutional neural network (CNN) for identifying real-time human feelings. The aim of the research study is to create a real-time emotion detection application by utilizing improved CNN. This research offers information on identifying emotions in films using deep learning techniques. Kinetic gas molecule optimization is used to optimize the fine-tuning and weights of CNN. This article describes the technique of the recognition process as well as its experimental validation. Two datasets such as video-based and image-based datasets, which are employed in many scholarly publications, are also investigated. The results of several emotion recognition simulations are provided, along with their performance factors.