Optimizing Facial Expression Recognition with Biogeography-Based Feature Selection
DOI:
https://doi.org/10.57159/gadl.jcmm.3.3.240119Keywords:
Facial Expression Recognition, Biogeography-Based Optimization, Feature Selection, Support Vector Machine, Computer VisionAbstract
Facial expression recognition is a challenging research field in computer vision due to various issues such as occlusion, lighting conditions, camera pose angles, and the selection of relevant features. Extracting and selecting pertinent features from facial images is crucial in achieving efficient expression recognition. This paper proposes a metaheuristic-based feature selection and classification methodology using the Biogeography-Based Optimization (BBO) algorithm to select the best-performing features and optimize the recognition accuracy of the classifier. The cross-validation recognition accuracy of the Support Vector Machine (SVM) is used as the evaluation criterion in the BBO algorithm to choose the optimal feature subset from the extracted features. The performance of the proposed BBO-SVM feature selection model is compared with other filter-based approaches. Experiments are conducted on three publicly available databases: JAFFE, MUG, and CK+, to validate the performance of the proposed system. The model achieves promising recognition accuracy across all datasets, with results compared to similar works presented in the literature.
References
G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Computers and Electrical Engineering, vol. 40, no. 1, pp. 16–28, Jan. 2014.
G. Sharma, R. Vig, and L. Singh, “Facial expression recognition with fused deep and geometric features,” International Journal of Disaster Recovery and Business Continuity, vol. 12, no. 1, pp. 930–944, 2021.
S. Gharsalli, B. Emile, H. Laurent, and X. Desquesnes, “Feature selection for emotion recognition based on random forest,” in Scitepress, Apr. 2016, pp. 610–617.
M. Ghosh, R. Guha, R. Sarkar, and A. Abraham, “A wrapper-filter feature selection technique based on ant colony optimization,” Neural Computing and Applications, vol. 32, no. 12, pp. 7839–7857, Jun. 2020.
S. M. Lajevardi and Z. M. Hussain, “Feature selection for facial expression recognition based on optimization algorithm,” in 2009 2nd International Workshop on Nonlinear Dynamics and Synchronization, IEEE, 2009, pp. 182–185.
S. M. Lajevardi and Z. M. Hussain, “Automatic facial expression recognition: Feature extraction and selection,” Signal Image and Video Processing, vol. 6, no. 1, pp. 159–169, Mar. 2012.
N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, and F. Battisti, European Workshop on Visual Information Processing, IEEE New Jersey, 2013.
N. Sreedharan, B. Ganesan, R. Raveendran, P. Sarala, B. Dennis, and R. Boothalingam, “Grey wolf optimisation-based feature selection and classification for facial emotion recognition,” IET Biometrics, vol. 7, no. 5, pp. 490–499, 2018.
J. C. Zwick and L. Wolkenstein, “Facial emotion recognition, theory of mind and the role of facial mimicry in depression,” Journal of Affective Disorders, vol. 210, pp. 90–99, Mar. 2017.
L. Zhang, K. Mistry, S. C. Neoh, and C. P. Lim, “Intelligent facial emotion recognition using moth-firefly optimization,” Knowledge-Based Systems, vol. 111, pp. 248–267, Nov. 2016.
X. Fan and T. Tjahjadi, “A dynamic framework based on local Zernike moment and motion history image for facial expression recognition,” Pattern Recognition, vol. 64, pp. 399–406, Apr. 2017.
Y. Yang, G. Wang, H. Kong, and P. Liatsis, “Self-learning facial emotional feature selection based on rough set theory,” Mathematical Problems in Engineering, 2009, p. 29.
J. Wei, R. Zhang, Z. Yu, R. Hu, J. Tang, C. Gui, and Y. Yuan, “A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection,” Applied Soft Computing, vol. 58, pp. 176–192, 2017.
S. Zhang, X. Zhao, and B. Lei, “Facial expression recognition based on local binary patterns and local fisher discriminant analysis,” WSEAS Transactions on Signal Processing, vol. 8, no. 1, pp. 21–31, 2012.
M. J. Lyons, S. Akamatsu, M. Kamachi, J. Gyoba, and J. Budynek, “The Japanese female facial expression (JAFFE) database,” in Proceedings of Third International Conference on Automatic Face and Gesture Recognition, 1998, pp. 14–16.
N. Aifanti and A. Delopoulos, “Linear subspaces for facial expression recognition,” Signal Processing: Image Communication, vol. 29, no. 1, pp. 177–188, 2014.
P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, IEEE, 2010, pp. 94–101.
Y.-Q. Wang, “An analysis of the Viola-Jones face detection algorithm,” Image Processing on Line, vol. 4, pp. 128–148, Jun. 2014.
A. Asthana, S. Zafeiriou, S. Cheng, and M. Pantic, “Incremental face alignment in the wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1859–1866.
G. Sharma, L. Singh, and S. Gautam, “Automatic facial expression recognition using combined geometric features,” 3D Research, vol. 10, no. 2, Jun. 2019.
P. A. Estévez, M. Tesmer, C. A. Perez, and J. M. Zurada, “Normalized mutual information feature selection,” IEEE Transactions on Neural Networks, vol. 20, no. 2, pp. 189–201, 2009.
R. M. Mehmood, R. Du, and H. J. Lee, “Optimal feature selection and deep learning ensembles method for emotion recognition from human brain EEG sensors,” IEEE Access, vol. 5, pp. 14797–14806, 2017.
D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702–713, 2008.
L. Goel, D. Gupta, and V. K. Panchal, “Two-phase anticipatory system design based on extended species abundance model of biogeography for intelligent battlefield preparation,” Knowledge-Based Systems, vol. 89, 2015.
W. L. Lim, A. Wibowo, M. I. Desa, and H. Haron, “A biogeography-based optimization algorithm hybridized with tabu search for the quadratic assignment problem,” Computational Intelligence and Neuroscience, 2016.
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
O. Chapelle, P. Haffner, and V. N. Vapnik, “Support vector machines for histogram-based image classification,” 1999.
M. Song, D. Tao, Z. Liu, X. Li, and M. Zhou, “Image ratio features for facial expression recognition application,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, no. 3, pp. 779–788, Jun. 2010.
M. B. Abdulrazaq, M. R. Mahmood, S. R. M. Zeebaree, M. H. Abdulwahab, R. R. Zebari, and A. B. Sallow, “An analytical appraisal for supervised classifiers’ performance on facial expression recognition based on Relief-F feature selection,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Mar. 2021.
J. Kumari, R. Rajesh, and K. M. Pooja, “Facial expression recognition: A survey,” in Procedia Computer Science, Elsevier, 2015, pp. 486–491.
M. A. Jaffar, “Facial expression recognition using hybrid texture features based ensemble classifier,” International Journal of Advanced Computer Science and Applications, vol. 8, no. 6, 2017.
Y. Rahulamathavan, R. C.-W. Phan, J. A. Chambers, and D. J. Parish, “Facial expression recognition in the encrypted domain based on local fisher discriminant analysis,” IEEE Transactions on Affective Computing, vol. 4, no. 1, pp. 83–92, 2012.
H. Ghazouani, “A genetic programming-based feature selection and fusion for facial expression recognition,” Applied Soft Computing, vol. 103, May 2021.
D. Ghimire, J. Lee, Z. N. Li, S. Jeong, and S. H. Park, “Recognition of facial expressions based on tracking and selection of discriminative geometric features,” International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 3, pp. 35–44, 2015.
Downloads
Published
How to Cite
License
Copyright (c) 2024 Journal of Computers, Mechanical and Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Journal of Computers, Mechanical and Management applies the CC Attribution- Non-Commercial 4.0 International License to its published articles. While retaining copyright ownership of the content, the journal permits activities such as downloading, reusing, reprinting, modifying, distributing, and copying of the articles, as long as the original authors and source are appropriately cited. Proper attribution is ensured by citing the original publication.
Accepted 2024-07-21
Published 2024-08-04
The journal is licensed under an 