An optimized facial emotion recognition architecture based on a deep convolutional neural network and genetic algorithm

Emotion recognition is one of the most interesting subjects in machine learning and computer vision fields, which is recognized by body language, speech, and face. Automatic emotion recognition is used in a variety of applications. In practice, recognizing human emotions with high accuracy is a challenging task. For this purpose, in this paper, we have recognized emotion from facial images using convolutional neural network architecture as one of the deep learning networks that used inception modules and dense blocks. The new proposed architecture is represented as GA-Dense-FaceliveNet, in which a genetic algorithm is expressed to tune the hyperparameters of the deep convolutional neural network. The proposed model is evaluated using three well-known datasets: CK + (extended Cohn-Kanade), JAFFE (Japanese Female Facial Expression), and KDEF (Karolinska Directed Emotional Faces). In the experiment, the accuracy of using CK + , JAFFE, and KDEF datasets is 99.96%, 98.92%, and 99.17%, respectively. The results demonstrate that the proposed method has higher performance compared to the state-of-the-art methods.