[1]
W. Ouarda, H. Trichili, A. M. Alimi, and B. Solaiman, Face recognition based on geometric features using support vector machines, in Proc. 6 th Int. Conf. Soft Computing and Pattern Recognition, Tunis, Tunisia, 2014, pp. 89–95.
[3]
X. Yuan, J. Lu, and T. Yahagi, A method of 3D face recognition based on principal component analysis algorithm, in Proc. 2005 IEEE Int. Symp. Circuits and Systems, Kobe, Japan, 2005, pp. 3211–3214.
[4]
X. Xie, Z. Yuan, W. Guo, and Y. Zhang, Color face image denoising based on noisy pixel detection and neighborhood weight interpolation, (in Chinese), Journal of Tsinghua University (Science & Technology), vol. 54, no. 4, pp. 536–539, 2014.
[6]
E. Liu and M. Zhi, Review of cross-age face recognition in discriminative models, in Proc. 8 th Int. Conf. Image, Vision and Computing, Dalian, China, 2023, pp. 124–130.
[7]
S. Chen, D. Zhang, L. Yang, and P. Chen, Age-invariant face recognition based on sample enhancement of generative adversarial networks, in Proc. 6th Int. Conf. Systems and Informatics, Shanghai, China, 2019, pp. 388–392.
[9]
C. Shi, J. Zhang, Y. Yao, Y. Sun, H. Rao, and X. Shu, CAN-GAN: Conditioned-attention normalized GAN for face age synthesis, Pattern Recognit. Lett., vol. 138, pp. 520–526, 2020.
[10]
A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, in Proc. 26 th Int. Conf. Neural Information Processing Systems, Lake Tahoe, NV, USA, 2012, pp. 1106–1114.
[11]
Y. Taigman, M. Yang, M. A. Ranzato, and L. Wolf, DeepFace: Closing the gap to human-level performance in face verification, in Proc. 2014 IEEE Conf. Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1701–1708.
[12]
Y. Li, G. Wang, L. Lin, and H. Chang, A deep joint learning approach for age invariant face verification, in Proc. CCCV Chinese Conf. Computer Vision, Xi'an, China, 2015, pp. 206–305.
[13]
Y. Wen, Z. Li, and Y. Qiao, Latent factor guided convolutional neural networks for age-invariant face recognition, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 4893–4901.
[14]
G. S. J. Hsu, H. Y. Wu, and M. H. Yap, A comprehensive study on loss functions for cross-factor face recognition, in Proc. 2020 IEEE/CVF Conf. Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, 2020, pp. 3604–3611.
[15]
X. Ren, J. Wang, and S. Li, MAM: Multiple attention mechanism neural networks for cross-age face recognition, Wireless Commun. Mobile Comput., vol. 2022, p. 8546029, 2022.
[16]
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, in Intelligent Signal Processing, S. Haykin and B. Kosko, eds. New York, NY, USA: Wiley-IEEE Press, 2001, pp. 306–351.
[17]
K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, presented at the 3rd Int. Conf. Learning Representations, San Diego, CA, USA, 2015.
[18]
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770–778.
[19]
V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu, Recurrent models of visual attention, in Proc. 27 th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2204–2212.
[20]
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu, CosFace: Large margin cosine loss for deep face recognition, in Proc. 2018 IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 5265–5274.
[21]
T. Xie, L. Yu, C. Luo, H. Xie, and Y. Zhang, Survey of deep face manipulation and fake detection, (in Chinese), Journal of Tsinghua University (Science & Technology), vol. 63, no. 9, pp. 1350–1365, 2023.
[22]
J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou, RetinaFace: Single-shot multi-level face localisation in the wild, in Proc. 2020 IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020, pp. 5202–5211.
[24]
Y. Wang and Q. Wu, Research on face recognition technology based on PCA and SVM, in Proc. 7 th Int. Conf. Big Data Analytics, Guangzhou, China, 2022, pp. 248–252.
[25]
Y. H. Lin, C. H. Tang, Z. T. Chen, G. S. J. Hsu, M. Shopon, and M. Gavrilova, Age-style and alignment augmentation for facial age estimation, in Proc. 19 th Int. Conf. Computer Analysis of Images and Patterns, Virtual Event, 2021, pp. 297–307.
[26]
D. Gong, Z. Li, D. Lin, J. Liu, and X. Tang, Hidden factor analysis for age invariant face recognition, in Proc. 2013 IEEE Int. Conf. Computer Vision, Sydney, Australia, 2013, pp. 2872–2879.