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


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.

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Deep belief network


Facial expression recognition


Gravitational search algorithm


Histogram oriented gradients


2D-Discrete Wavelet transform


Dimensionality reduction


Support-vector machines


Deep convolutional neural network, discrete Beamlet transform regularization-blind restoration


Multi-signal convolutional neural network part-based hierarchical bidirectional neural network


Cohn Kanade


Multi-channel deep spatial–temporal feature neural network


Man–machine interaction


Part-based hierarchical bidirectional neural network


Multi-signal convolutional neural network


Oulu-Chinese Academy of Science Institute of Automation


Spider monkey optimization


Discrete Cosine transform


Extended denver intensity of spontaneous facial actions




Restricted Boltzmann machine


Gradient and orientation


Low pass filter


High pass filter


Principal component analysis


Dimensionality reduction


Hinton contrast divergence


Locally linear embedding


t-Distributed stochastic neighbor embedding


Receiver operating characteristic curve


Area under the ROC curve


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The Authors extend their warm thanks to Deanship of Scientific Research (DSR), King Saud University, Riyadh, Saudi Arabia for permitting us to carry out the research and also for acknowledging the financial aid from DSR through the Project Group No. RG-1441-343.

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Correspondence to Wael Mohammad Alenazy.

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Alenazy, W.M., Alqahtani, A.S. Gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition. J Ambient Intell Human Comput (2020).

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  • Facial expression recognition
  • Deep belief network
  • Semi-supervised approach
  • Gravitational search algorithm
  • Histogram oriented gradients