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

• Published in: Signal, image and video processing Vol. 18; no. 2; pp. 1119 - 1129
• Main Authors: Aghabeigi, Fereshteh, Nazari, Sara, Osati Eraghi, Nafiseh
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2024; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Imaging; Computer Science; Computer vision; Datasets; Deep learning; Emotion recognition; Emotions; Genetic algorithms; Image Processing and Computer Vision; Machine learning; Multimedia Information Systems; Neural networks; Original Paper; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Vision; Deep learning; Emotion recognition; Genetic algorithm; Convolutional neural network; Facial emotion recognition
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-023-02764-z

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.