Automatic Facial Emotion Recognition Using Hybrid Deep Learning Approach

Facial emotion identification is an important task for human-computer interaction, self-driving vehicles, and a wide range of multimedia applications. This study proposes an interconnected framework for recognizing human facial emotions. The architecture includes two methods for machine learning (identification and categorization) that can be learned offline for real-time applications. We begin by experimenting with the ResNet 50 classifier to find faces in photos. We then extract all of the features that reflect the characteristics of a face through specific appearance information. Following that, as a feature reduction stage, we used a genetic search optimization strategy. For emotion categorization, we utilize a BayesNet classifier with a latent the state of emotion that handles mis-/false observations. Furthermore, the proposed approach for recognizing emotion is gender- and facial skincolor independent. The study proposes an enhanced hybrid deep learning strategy that evaluates facial expressions in a picture to predict sentiments employing a convolution neural network (CNN). The algorithm that was created in this study adopts a hybrid CNN methodology for determining if the picture's fundamental emotion is happiness or sadness. The proposed algorithm was trained using the FER2013 dataset, and outcomes indicate that it is more effective than current state-of-the-art methods of determining emotions from facial expressions. The overall accuracy, precision, sensitivity, specificity and AVC of 91%, 93%, 90%, 93% and 0.95 is attained for emotion stated as ‘happy’. For emotion stated as ‘sad’ the accuracy, precision, sensitivity, specificity and AUC of 91%, 90%, 93%, 90% and 0.95 is attained.