Email this article 
A Comparative Evaluation of State-of-the-Art Deep Face Recognition Models on the Indonesian Muslim Student Face Dataset
Abstract
identification and verification protocols, with metrics such as CMC, ROC/DET, EER, and decidability index, along with computational profiling. Results show AdaFace-R50 achieves the best rank-1 accuracy at 91.70% (95% CI: [90.98%, 92.38%]), significantly outperforming others. Performance varies across subsets, with A2 reaching ≥99.40% accuracy, while B1 is most challenging (≤86.05%). These findings stress the importance of diverse datasets and highlight the effectiveness of modern margin-based loss functions.
Keywords
Full Text:
PDFReferences
Boutros, F., Damer, N., Kirchbuchner, F., Kuijper, A., 2022. ElasticFace: Elastic margin loss for deep face recognition. Proc. CVPRW, pp. 1578–1587. doi:10.1109/CVPRW56347.2022.00164.
Buolamwini, J., Gebru, T., 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. Proc. FAT*, PMLR, pp. 77–91.
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A., 2018. VGGFace2: A dataset for recognising faces across pose and age. Proc. IEEE FG, pp. 67–74. doi:10.1109/FG.2018.00020.
Chopra, S., Hadsell, R., LeCun, Y., 2005. Learning a similarity metric discriminatively, with application to face verification. Proc. CVPR, pp. 539–546. doi:10.1109/CVPR.2005.202.
Deng, J., Guo, J., Xue, N., Zafeiriou, S., 2019. ArcFace: Additive angular margin loss for deep face recognition. Proc. CVPR, pp. 4690–4699. doi:10.1109/CVPR.2019.00482.
Grother, P., Ngan, M., Hanaoka, K., 2019. Face recognition vendor test (FRVT) part 3: Demographic effects. NIST Interagency Report 8280, 1–82. doi:10.6028/NIST.IR.8280.
He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. Proc. CVPR, pp. 770–778. doi:10.1109/CVPR.2016.90.
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E., 2007. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst, 1–11.
Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E., 2016. The MegaFace benchmark: 1 million faces for recognition at scale. Proc. CVPR, pp. 4873–4882. doi:10.1109/CVPR.2016.527.
Kim, M., Jain, A.K., Liu, X., 2022. AdaFace: Quality adaptive margin for face recognition. Proc. CVPR, pp. 18750–18759. doi:10.1109/CVPR52688.2022.01819.
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L., 2017. SphereFace: Deep hypersphere embedding for face recognition. Proc. CVPR, pp. 6738–6746. doi:10.1109/CVPR.2017.713.
McNemar, Q., 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12, 153–157. doi:10.1007/BF02295996.
Meng, Q., Zhao, S., Huang, Z., Zhou, F., 2021. MagFace: A universal representation for face recognition and quality assessment. Proc. CVPR, pp. 14225–14234. doi:10.1109/CVPR46437.2021.01400.
Parkhi, O.M., Vedaldi, A., Zisserman, A., 2015. Deep face recognition. Proc. BMVC, pp. 41.1–41.12. doi:10.5244/C.29.41.
Purnawansyah, P., Wibawa, A.P., Widyaningtyas, T., Haviluddin, H., Darwis, H., Azis, H., Ali, Z., 2023. Indonesian muslim student face dataset (IMSFD). doi:10.17632/f6f3y6ndgw.1.
Schroff, F., Kalenichenko, D., Philbin, J., 2015. FaceNet: A unified embedding for face recognition and clustering. Proc. CVPR, pp. 815–823. doi:10.1109/CVPR.2015.7298682.
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A., 2017. Inception-v4, Inception-ResNet and the impact of residual connections on learning. Proc. AAAI, pp. 4278–4284. doi:10.1609/aaai.v31i1.11231.
Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W., 2018. CosFace: Large margin cosine loss for deep face recognition. Proc. CVPR, pp. 5265–5274. doi:10.1109/CVPR.2018.00552.
Wang, M., Deng, W., 2021. Deep face recognition: A survey. Neurocomputing 429, 215–244. doi:10.1016/j.neucom.2020.10.081.
Yi, D., Lei, Z., Liao, S., Li, S.Z., 2014. Learning face representation from scratch. arXiv preprint arXiv:1411.7923. doi:10.48550/arXiv.1411.7923.
Zeng, D., Veldhuis, R., Spreeuwers, L., 2021. A survey of face recognition techniques under occlusion. IET Biometrics 10, 581–606. doi:10.1049/bme2.12029.
Zhu, Z., Huang, G., Deng, J., et al., 2021. WebFace260M: A benchmark unveiling the power of million-scale deep face recognition. Proc. CVPR, pp. 10492–10502. doi:10.1109/CVPR46437.2021.01035.
DOI: https://doi.org/10.17509/coelite.v5i1.98428
Refbacks
- There are currently no refbacks.
Journal of Computer Engineering, Electronics and Information Technology (COELITE)
is published by UNIVERSITAS PENDIDIKAN INDONESIA (UPI),
and managed by Department of Computer Enginering.
Jl. Dr. Setiabudi No.229, Kota Bandung, Indonesia - 40154
email: [email protected]
e-ISSN: 2829-4149
p-ISSN: 2829-4157