Modelling an efficient hybridized approach for facial emotion recognition using unconstraint videos and deep learning approaches

Facial Emotion detection (FER) is primarily used to assess human emotion to meet the demands of several real-time applications, including emotion detection, computer-human interfaces, biometrics, forensics, and human-robot collaboration. However, several current techniques fall short of providing accurate predictions with a low error rate. This study focuses on modeling an effective FER with unrestricted videos using a hybrid SegNet and ConvNet model. SegNet is used to segment the regions of facial expression, and ConvNet is used to analyze facial features and to make predictions about emotions like surprise, sadness, and happiness, among others. The suggested hybridized approach uses a neural network model to classify face characteristics depending on their location. The proposed model aims to recognize facial emotions with a quicker convergence rate and improved prediction accuracy. This work takes into account the internet-accessible datasets from the FER2013, Kaggle, and GitHub databases to execute execution. To accomplish generalization and improve the quality of prediction, the model acts as a multi-modal application. With the available datasets throughout the testing procedure, the suggested model provides 95% prediction accuracy. Additionally, the suggested hybridized model is used to calculate the system's importance. The experimental results show that, in comparison to previous techniques, the expected model provides superior prediction results and produces better trade-offs. Other related statistical measures are also assessed and contrasted while the simulation is being run in the MATLAB 2020a environment.