
Arab Face Recognition and Identification Based on Ethnicity and Gender Using Machine Learning
Researchers are highly interested in the classification of ethnicity using the human face since every individual has features that distinguish him from others, and every group of people shares some features that set them apart. These features are called ethnicity. A shortage of academic inquiry into the Arab world is well acknowledged. To achieve this, this research seeks to generate an Arab dataset by first grouping all Arab countries into similar categories and then classifying these labels using machine learning methods. The Arab face dataset created consists of five labels: Arab Gulf States, Egypt, Levant, Maghreb, and North and East Arab African Countries. This paper uses six types of Machine Learning to classify gender and ethnicity: Artificial Neural Network (ANN), logistic regression, Support Vector Machine (SVM), naïve bayes, K-Nearest Neighbors (KNNs), and random forest. SVM model has recorded the best result to classify gender and ethnicity with 92.7% Area Under the Curve (AUC) and 57.6% accuracy, and ANN model has recorded the best result to classify ethnicity with 92.2% AUC and 72.2% accuracy.
[1] Acien A., Morales A., Vera-Rodriguez R., Bartolome I., and Fierrez J., “Measuring the Gender and Ethnicity Bias in Deep Models for Face Recognition,” in Proceedings of the 23rd Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Madrid, pp. 584-593, 2019. https://doi.org/10.1007/978-3-030-13469-3_68
[2] Al-Dabbas H., Azeez R., and Ali A., “Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence,” Engineering, Technology and Applied Science Research, vol. 14, no. 2, pp. 13706-13713, 2024. https://doi.org/10.48084/etasr.7002
[3] Al-Humaidan N. and Prince M., “A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach,” IEEE Access, vol. 9, pp. 50755-50766, 2021. DOI:10.1109/ACCESS.2021.3069022
[4] Anwar I. and Islam N., “Learned Features are Better for Ethnicity Classification,” Cybernetics and Information Technologies, vol. 17, no. 3, pp. 152-164, 2017. DOI:10.1515/cait-2017-0036
[5] Awad G., Hashem H., and Nguyen H., “Identity and Ethnic/Racial Self-Labeling among Americans of Arab or Middle Eastern and North African Descent,” Identity, vol. 21, no. 2, pp. 115- 130, 2021. https://doi.org/10.1080/15283488.2021.1883277
[6] Chen H., Deng Y., and Zhang S., “Where Am I From? -East Asian Ethnicity Classification from Facial Recognition,” Project Study in Stanford University, pp. 1-5, 2016. https://api.semanticscholar.org/CorpusID:292589 43
[7] Darabant A., Borza D., and Danescu R., “Recognizing Human Races through Machine Learning-A Multi-Network, Multi-Features Study,” Mathematics, vol. 9, no. 2, pp. 1-19, 2021. https://doi.org/10.3390/math9020195
[8] Das A., Dantcheva A., and Bremond F., “Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-Task Convolution Neural Network Approach,” in Proceedings of the European Conference of Computer Vision Workshops, Munich, pp. 1-14, 2018. https://doi.org/10.1007/978-3-030-11009-3_35
[9] Gudi A., “Recognizing Semantic Features in Faces Using Deep Learning,” arXiv Preprint, vol. arXiv:1512.00743, pp. 1-9, 2015. https://doi.org/10.48550/arXiv.1512.00743
[10] Heng Z., Dipu M., and Yap K., “Hybrid Supervised Deep Learning for Ethnicity Classification Using Face Images,” in Proceedings of the IEEE International Symposium on Circuits and Systems, Florence, pp. 1-5, 2018. DOI:10.1109/ISCAS.2018.8351370
[11] Kanwar A. and Singh K., “Prediction of Age, Gender, and Ethnicity Using CNN and Facial Images in Real-Time,” in Proceedings of the IEEE World Conference on Applied Intelligence and Computing, Sonbhadra, pp. 668-674, 2023. https://ieeexplore.ieee.org/document/10263824
[12] Karkkainen K. and Joo J., “Fairface: Face Attribute Dataset for Balanced Race, Gender, and Age,” arXiv Preprint, vol. arXiv:1908.04913, pp. 1-11, 2019. https://doi.org/10.48550/arXiv.1908.04913
[13] Karkkainen K. and Joo J., “FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Hawaii, pp. 1548-1558, 2021. DOI:10.1109/WACV48630.2021.00159
[14] Khan K., Khan R., Ali J., Uddin I., Khan S., and Roh B., “Race Classification Using Deep Learning,” Computers, Materials and Continua, vol. 68, no. 3, pp. 3483-3498, 2021. DOI:10.32604/cmc.2021.016535
[15] Lakshmiprabha N., “Face Image Analysis Using AAM, GABOR, LBP and WD Features for Gender, Age, Expression and Ethnicity Classification,” arXiv Preprint, vol. arXiv:1604.01684, pp. 1-16, 2016. https://doi.org/10.48550/arXiv.1604.01684
[16] Lu C., Ahmed R., Lamri A., and Anand S., “Use of Race, Ethnicity, and Ancestry Data in Health Research,” PLOS Global Public Health, vol. 2, no. 9, pp. 1-15, 2022. DOI:10.1371/journal.pgph.0001060
[17] Masood S., Gupta S., Wajid A., Gupta S., and Ahmed M., “Prediction of Human Ethnicity from Facial Images Using Neural Networks,” in Proceedings of the Advances in Intelligent Systems and Computing, Cairo, pp. 217-226, 2018. https://doi.org/10.1007/978-981-10-3223- 3_20
[18] Molina D., Causa L., and Tapia J., “Reduction of Bias for Gender and Ethnicity from Face Images Using Automated Skin Tone Classification,” in Proceedings of the International Conference of the Biometrics Special Interest Group, Darmstadt, Arab Face Recognition and Identification Based on Ethnicity and Gender Using ... 707 pp. 1-5, 2020. https://ieeexplore.ieee.org/document/9211042
[19] Narang N. and Bourlai T., “Gender and Ethnicity Classification Using Deep Learning in Heterogeneous Face Recognition,” in Proceedings of the International Conference on Biometrics, Halmstad, pp. 1-8, 2016. DOI:10.1109/ICB.2016.7550082
[20] Reddy N., Rao M., and Satyanarayana C., “A Novel Face Recognition System by the Combination of Multiple Feature Descriptors,” The International Arab Journal of Information Technology, vol. 16, no. 4, pp. 669-676, 2019. https://iajit.org/PDF/July%202019,%20No.%204 /11890.pdf
[21] Ricanek K. and Tesafaye T., “MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,” in Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, Southampton, pp. 341-345, 2006. DOI:10.1109/FGR.2006.78
[22] Rothe R., Timofte R., and Van Gool L., “Deep Expectation of Real and Apparent Age from a Single Image without Facial Landmarks,” International Journal of Computer Vision, vol. 126, no. 2, pp. 144-157, 2018. https://doi.org/10.1007/s11263-016-0940-3
[23] Srinivas N., Atwal H., Rose D., Mahalingam G., Ricanek K., and Bolme D., “Age, Gender, and Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for the East Asian Face Dataset,” in Proceeding of the 12th IEEE International Conference on Automatic Face and Gesture Recognition, Washington (DC), pp. 953- 960, 2017. DOI:10.1109/FG.2017.118
[24] Sunitha G., Geetha K., Neelakandan S., Pundir A., Hemalatha S., and Kumar V., “Intelligent Deep Learning Based Ethnicity Recognition and Classification Using Facial Images,” Image and Vision Computing, vol. 121, pp. 104404, 2022. https://doi.org/10.1016/j.imavis.2022.104404
[25] Trivedi A. and Amali D., “A Comparative Study of Machine Learning Models for Ethnicity Classification,” in Proceedings of the IOP Conference Series: Materials Science and Engineering, Busan, pp. 1-8, 2017. DOI:10.1088/1757-899X/263/4/042091
[26] Wang M. and Deng W., “Deep Face Recognition: A Survey,” Neurocomputing, vol. 429, pp. 215- 244, 2021. https://doi.org/10.1016/j.neucom.2020.10.081
[27] Wang W., He F., and Zhao Q., “Facial Ethnicity Classification with Deep Convolutional Neural Networks,” in Proceedings of the 11th Biometric Recognition Chinese Conference, Chengdu, pp. 176-185, 2016. https://doi.org/10.1007/978-3- 319-46654-5_20
[28] Zhang Z., Song Y., and Qi H., “Age Progression/Regression by Conditional Adversarial Autoencoder,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, pp. 5810-5818, 2017. DOI:10.1109/CVPR.2017.463 708