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

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Abstract

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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Abbreviations

DBN:

Deep belief network

FER:

Facial expression recognition

GSA:

Gravitational search algorithm

HOG:

Histogram oriented gradients

2D-DWT:

2D-Discrete Wavelet transform

DR:

Dimensionality reduction

SVM:

Support-vector machines

DBTR-BR:

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

MSCNN-PHRNN:

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

CK+:

Cohn Kanade

MDSTFN:

Multi-channel deep spatial–temporal feature neural network

MMI:

Man–machine interaction

PHRNN:

Part-based hierarchical bidirectional neural network

MSCNN:

Multi-signal convolutional neural network

Oulu-CASIA:

Oulu-Chinese Academy of Science Institute of Automation

SMO:

Spider monkey optimization

DCT:

Discrete Cosine transform

DISFA+:

Extended denver intensity of spontaneous facial actions

EEG:

Electroencephalography

RBM:

Restricted Boltzmann machine

GO:

Gradient and orientation

LPF:

Low pass filter

HPF:

High pass filter

PCA:

Principal component analysis

(DR):

Dimensionality reduction

(CD):

Hinton contrast divergence

(LLE):

Locally linear embedding

(t-SNE):

t-Distributed stochastic neighbor embedding

(ROC):

Receiver operating characteristic curve

(AUC):

Area under the ROC curve

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Acknowledgment

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 12, 1631–1646 (2021). https://doi.org/10.1007/s12652-020-02235-0

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