References
- Abbas, Q., Albalawi, T.S., Perumal, G. and Celebi, M.E. (2023). ‘Automatic Face Recognition System Using Deep Convolutional Mixer Architecture and AdaBoost Classifier’. Applied Sciences, 13(17), 9880.
- Ge, Y., Liu, H., Du, J., Li, Z. and Wei, Y. (2023). ‘Masked face recognition with convolutional visual self-attention network’. Neurocomputing, 518, 496–506.
- El Kaddouhi, S., Saaidi, A. and Abarkan, M. (2018). ‘Eye detection based on Viola and Jones Detector, skin color, and eye template’. International Journal of Control and Automation, 11(5), 59–72.
- Hariri, W. (2022). ‘Efficient masked face recognition method during the COVID-19 pandemic’. Signal Image Video Process, 16, 605–612.
- Mishra, N.K. and Singh, S.K. (2022). ‘Regularized Hardmining loss for face recognition’. Image Vis. Comput., 117, 104343.
- Hasan, K., Ahsan, S., Mamun, A.A., Newaz, S.H.S. and Lee, G.M. (2021). ‘Human Face Detection Techniques: A Comprehensive Review and Future Research Directions’. Electronics, 10, 2354.
- Damer, N., Boutros, F., Süßmilch, M., Fang, M., Kirchbuchner, F. and Kuijper, A. (2021). ‘Masked face recognition: Human vs. machine’. arXiv, arXiv:2103.01924.
- Zhang, A., Shan, S., Gao, W., Chen, X. and Zhang, H. (2005). ‘Local gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition’. In Tenth IEEE International Conference on Computer Vision, 786–791.
- Ekenel, H.K. and Stiefelhagen, R. (2009). ‘Why is facial occlusion a challenging problem?’. In International Conference on Biometrics, 299–308.
- Zou, X., Kittler, J. and Messer, K. (2007). ‘Illumination Invariant Face Recognition: A Survey’. In First IEEE International conference on Biometrics: theory, applications, and Systems, 1–8.
- Gross, R., Baker, S., Matthews, I. and Kanade, T. (2005). ‘Face Recognition across Pose and Illumination’. In Handbook of Face Recognition. Springer, New York, NY, 193–216.
- Ling, H., Soatto, S., Ramanathan, N. and Jacobs, D.W. (2007). ‘A study of face recognition as people age’. In IEEE 11th International Conference on Computer Vision, 1–8.