Gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition

• Published in: Journal of ambient intelligence and humanized computing Vol. 12; no. 2; pp. 1631 - 1646
• Main Authors: Alenazy, Wael Mohammad, Alqahtani, Abdullah Saleh
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Belief networks; Classification; Classifiers; Computational Intelligence; Datasets; Deep learning; Discrete Wavelet Transform; Electroencephalography; Emotions; Engineering; Face recognition; Feature extraction; Machine learning; Neural networks; Original Research; Principal components analysis; Robotics and Automation; Search algorithms; User Interfaces and Human Computer Interaction; Facial expression recognition; Gravitational search algorithm; Semi-supervised approach; Deep belief network; Histogram oriented gradients
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-02235-0

Facial expression recognition (FER) is an essential part of effective human–computer interaction and serves as a helpful medium for children and patients who have problems with communication. However, most of the previous studies focus on building a FER model based on supervised and unsupervised approaches. This paper is focused on a semi-supervised deep belief network (DBN) approach to predict the facial expressions from the CK+, Oulu CASIA, MMI, and JAFFE datasets. To achieve accurate classification of the facial expressions, a gravitational search algorithm (GSA) is applied to optimize some parameters in the DBN network. The Histogram oriented gradients (HOG) and 2D-Discrete Wavelet Transform (2D-DWT) are used for feature extraction from the lip, cheek, brow, eye, and furrow patches. The unwanted information present in the image is eliminated using a feature selection approach. The feature extraction is done by the Kernel-principal component analysis to obtain higher-order correlations between input variables and detect non-linear components. The HOG features extracted from the lip patch provides the best performance for accurate facial expression classification. Finally, a comparative analysis to compare the proposed model with different machine learning techniques based on the evaluation criteria. The results demonstrate that DBN-GSA based classifier is more accurate than the rest of the classifiers.