AIFR: Face Recognition Research Based on Age Factor Characteristics
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0862-8_22
Reference15 articles.
1. Ouarda, W., Trichili, H., Alimi, A.M., Solaiman, B.: Face recognition based on geometric features using support vector machines. In: 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 89–95. IEEE (2014)
2. Filali, H., Riffi, J., Mahraz, A.M., Tairi, H.: Multiple face detection based on machine learning. In: 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), pp. 1–8. IEEE (2018)
3. Chen, S., Zhang, D., Yang, L., Chen, P.: Age-invariant face recognition based on sample enhancement of generative adversarial networks. In: 2019 6th International Conference on Systems and Informatics (ICSAI), pp. 388–392. IEEE (2019)
4. Wang, D.-C., Tsai, Z.-J., Chen, C.-C., Horng, G.-J.: Development of a face prediction system for missing children in a smart city safety network. Electronics 11(9), 1440 (2022)
5. Zhao, S., Li, J., Wang, J.: Disentangled representation learning and residual GAN for age-invariant face verification. Pattern Recogn. 100, 107097 (2020)
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