An Intelligent Facial Expression Recognition System Using a Hybrid Deep Convolutional Neural Network for Multimedia Applications
Abstract
:1. Introduction
2. Literature Review
Deep-Learning-Based Face Recognition
3. Methodology
3.1. Image Resizing
3.2. Spatiotemporal Feature Learning with CNNs
3.3. Spatiotemporal Fusion with DBNs
- As a bottom-up method of unsupervised pretraining, greedy layer-wise training is used. The logarithm of the probability of derivatives is used to update the weights of each RBM model:
- 2.
- Network parameters are updated through the supervised fine-tuning stage with backpropagation. Specifically, supervised fine-tuning can be achieved by comparing input data to the reconstructed data using the following loss function.
3.4. Classification
4. Experiments Analysis
4.1. Dataset
4.2. Performance Metric
4.3. Analysis and Discussion of Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | FE | AN | DI | HA | SA | SU | NE | Total | Resolution |
---|---|---|---|---|---|---|---|---|---|
JAFFE | 30 | 30 | 28 | 29 | 30 | 30 | 30 | 207 | 256 × 256 |
KDEF | 140 | 140 | 140 | 140 | 140 | 140 | 140 | 980 | 562 × 762 |
RaFD | 67 | 67 | 67 | 67 | 67 | 67 | 67 | 469 | 681 × 1024 |
Classes | JAFFE | KDEF | RaFD | ||||||
---|---|---|---|---|---|---|---|---|---|
Precision | Recall | F1-Score | Precision | Recall | F1-Score | Precision | Recall | F1-Score | |
NE | 97.49 | 98.1 | 98.97 | 95.88 | 96.3 | 96.23 | 97.14 | 99.0 | 0.9832 |
AN | 99.1 | 99.3 | 99.00 | 94.97 | 93.8 | 94.18 | 98.29 | 99.8 | 99.48 |
DI | 97.73 | 97.2 | 97.43 | 94.11 | 95.4 | 94.97 | 95.97 | 96.9 | 96.90 |
FE | 99.82 | 99.4 | 8.96 | 95.55 | 96.2 | 96.51 | 99.08 | 98.3 | 98.39 |
HA | 96.39 | 97.9 | 97.11 | 95.80 | 94.2 | 95.12 | 98.95 | 99.2 | 99.56 |
SA | 99.97 | 99.7 | 99.58 | 97.04 | 97.2 | 97.10 | 98.85 | 98.5 | 98.47 |
SU | 95.79 | 95.6 | 95.71 | 95.27 | 95.8 | 95.83 | 99.50 | 99.1 | 98.84 |
Authors | Reference Number | Year of Study | Accuracy (%) |
---|---|---|---|
Liang, Dong, et al. | [67] | 2005 | 95 |
Zhao, X., and Zhang, S | [68] | 2012 | 77.14 |
Islam, et al. | [71] | 2018 | 93.51 |
Eng, S.K., et al. | [70] | 2019 | 79.19 |
Jain, et al. | [57] | 2019 | 95.23 |
Shah, et al. | [65] | 2020 | 93.96 |
Barman, A., and Paramartha D.a | [66] | 2021 | 96.4 |
Kas, et al. | [69] | 2021 | 56.67 |
Yaddaden, Y. | [72] | 2023 | 96.17 |
Ahmed J. and Hassanain K. | Proposed model | 2023 | 98.14 |
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Obaid, A.J.; Alrammahi, H.K. An Intelligent Facial Expression Recognition System Using a Hybrid Deep Convolutional Neural Network for Multimedia Applications. Appl. Sci. 2023, 13, 12049. https://doi.org/10.3390/app132112049
Obaid AJ, Alrammahi HK. An Intelligent Facial Expression Recognition System Using a Hybrid Deep Convolutional Neural Network for Multimedia Applications. Applied Sciences. 2023; 13(21):12049. https://doi.org/10.3390/app132112049
Chicago/Turabian StyleObaid, Ahmed J., and Hassanain K. Alrammahi. 2023. "An Intelligent Facial Expression Recognition System Using a Hybrid Deep Convolutional Neural Network for Multimedia Applications" Applied Sciences 13, no. 21: 12049. https://doi.org/10.3390/app132112049
APA StyleObaid, A. J., & Alrammahi, H. K. (2023). An Intelligent Facial Expression Recognition System Using a Hybrid Deep Convolutional Neural Network for Multimedia Applications. Applied Sciences, 13(21), 12049. https://doi.org/10.3390/app132112049