International Scientific Journal


In recent times, computer vision related face image analysis has gained significant attention in various applications namely biometrics, surveillance, security, data retrieval, informatics, etc. The main objective of the facial analysis is to extract facial soft biometrics like expression, identity, age, ethnicity, gender, etc. Of these, ethnicity recognition is considered a hot search topic, a major part of community with deep connections to many social and ecological concerns. The deep learning and machine learning methods is merit for effective ethnicity classification and recognition. This study develops a facial imaging based ethnicity recognition using equilibrium optimizer with machine learning (FIER-EOML) model. The goal of the FIER-EOML technique is to detect and classify different kinds of ethnicities on facial images. To accomplish this, the presented FIER-EOML technique applies an EfficientNet model to generate a set of feature vectors. For ethnicity recognition, the presented model uses long short-term memory method. To improve the recognition performance, the FIER-EOML technique utilizes EO algorithm for hyperparameter tuning process. The performance validation of the FIER-EOML technique is tested on BUPT-GLOBALFACE dataset and the results are examined under several measures. The comprehensive comparison study reported the enhanced performance of the FIER-EOML technique over other recent approaches.
PAPER REVISED: 2022-11-17
PAPER ACCEPTED: 2022-11-21
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Special issue 1, PAGES [353 - 364]
  1. Belcar, D., et al., Automatic Ethnicity Classification from Middle Part of the Face Using Convolutional Neural Networks, Informatics, 9 (2022), 1, p. 18
  2. Devaraj, S. J., et al., Deep Learning Based Facial Feature Detection for Ethnicity Recognition, Smart Computing Techniques and Applications, Springer, Singapore, 2021, pp. 527-534
  3. Noseworthy, P. A., et al., Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis, Circulation: Arrhythmia and Electrophysiology, 13 (2020), 3, e007988
  4. Liu, A., et al., Cross‐Ethnicity Face Anti‐Spoofing Recognition Challenge: A Review, IET Biometrics, 10 (2021), 1, pp. 24-43
  5. Wang, C., et al., Expression of Concern: Facial Feature Discovery for Ethnicity Recognition, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9 (2019), 1, e1278
  6. Dahlan, H. A.,. A Survey on Deep Learning Face Age Estimation Model: Method and Ethnicity, International Journal of Advanced Computer Science and Applications, 12 (2021), 11
  7. Amali, G., et al., A Deep Learning-Based Framework for Accurate Facial Ethnicity Classification and Efficient Query Retrieval, in: Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, IGI Global, Hershey, Penn., USA, 2021, pp. 216-240
  8. Li, J., et al., Machine Learning Approach to Predict Race and Ethnicity of Twitter Users Discussing HIV Prep Prevention Therapy, Proceedings, APHA Annual Meeting and Expo, Denver, Col., USA, 2021
  9. Greco, A., et al., Benchmarking Deep Network Architectures for Ethnicity Recognition Using a New Large Face Dataset, Machine Vision and Applications, 31 (2020), 7, pp. 1-13
  10. Aworinde, H. O., et al., Performance Evaluation of Feature Extraction Techniques in Multi-Layer-Based Fingerprint Ethnicity Recognition System, Asian J. of Research in Computer Sci., 3 (2019), 1, pp. 1-9
  11. AlBdairi, A. J. A., et al., Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification, Applied Sciences, 12 (2022), 5, 2605
  12. Sunitha, G., et al., Intelligent Deep Learning-Based Ethnicity Recognition and Classification Using Facial Images, Image and Vision Computing, 121 (2022), 104404
  13. Terada, T., et al., Three-Dimensional Facial Ethnicity Identification Based on Cylindrical Projection and Deep Learning, Proceedings, IEEE International Conference on Consumer Electronics, Las Vegas, Nev, USA, 2022, pp. 01-04
  14. Ng, W. W., et al., Fine-Grained Facial Ethnicity Recognition Based on Dual Convolutional Autoencoders, Proceedings, 11th International Conference on Intelligent Control and Information Processing, Dali, China, 2021, pp. 235-240
  15. Lei, Q., et al., Research on Image Recognition Method of Ethnic Costume Based on VGG, Proceedings, International Conference on Machine Learning for Cyber Security, Guangzhou, China, 2020, pp. 312-325
  16. Christy, C., et al., Deep Learning with Chaotic Encryption Based Secured Ethnicity Recognition, Proceedings, 3rd International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, pp. 515-520
  17. Heng, Z., et al., Hybrid Supervised Deep Learning for Ethnicity Classification Using Face Images, Proceedings, IEEE International Symposium on Circuits and Systems, Florence, Italy, pp. 1-5
  18. Shah, H. A., et al., A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet, IEEE Access, 10 (2022), June, pp. 65426-65438
  19. Alabdulkreem, E., et al., Optimal Weighted Fusion-Based Insider Data Leakage Detection and Classification Model for Ubiquitous Computing Systems, Sustainable Energy Technologies and Assessments, 54 (2022), 102815
  20. Alsubai, S., et al., Bald Eagle Search Optimization with Deep Transfer Learning Enabled Age-Invariant Face Recognition Model, Image and Vision Computing, 126 (2022), 104545
  21. Abukhodair, F., et al., An Intelligent Metaheuristic Binary Pigeon Optimization-Based Feature Selection and Big Data Classification in a MapReduce Environment, Mathematics, 9 (2021), 20, 2627
  22. Mansour, R. F., et al., Optimal Deep Learning-Based Fusion Model for Biomedical Image Classification, Expert Systems, 39 (2022), 3, e12764
  23. Tirado-Martin, P., Sanchez-Reillo, R., BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets, Applied Sciences, 11 (2021), 13, 5880
  24. Yu, B. C., Shao, L. S., An Optimization Method of Mine Ventilation System Based on R2 Index Hybrid Multi-Objective Equilibrium Optimization Algorithm, Energy Reports, 8 (2022), Nov., pp. 11003-11021

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence