[1] CAO Q, SHEN L, XIE W, et al. Vggface2: A dataset for recognising faces across pose and age[C]//2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018). Xi'an, China: IEEE, 2018: 67-74.
[3] GUO Y, ZHANG L, HU Y, et al. Ms-celeb-1m: a dataset and benchmark for large-scale face recognition[C]//European conference on computer vision. Amsterdam, Netherlands: Springer, Cham, 2016: 87-102.
[5] TAIGMAN Y, YANG M, RANZATO M A, et al. Deepface: closing the gap to human-level performance in face verification[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA: IEEE, 2014: 1701-1708.
[6] ANTIPOV G, BACCOUCHE M, DUGELAY J L. Face aging with conditional generative adversarial networks[C]//2017 IEEE international conference on image processing (ICIP). Piscataway, NJ: IEEE, 2017: 2089-2093.
[7] ZHANG Z, SONG Y, QI H. Age progression/regression by conditional adversarial autoencoder[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017: 5810-5818.
[8] GONG D, LI Z, LIN D, et al. Hidden factor analysis for age invariant face recognition[C]// 2013 IEEE International Conference on Computer Vision (ICCV). Piscataway, NJ: IEEE, 2013: 2872-2879.
[9] WANG H, GONG D, LI Z, et al. Decorrelated adversarial learning for age-invariant face recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019: 3527-3536.
[10] HUANG Z, ZHANG J, SHAN H. When age-invariant face recognition meets face age synthesis: A multi-task learning framework[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA: IEEE, 2021: 7282-7291.
[11] WU H, XIAO B, CODELLA N, et al. Cvt: introducing convolutions to vision transformers[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada: IEEE, 2021: 22-31.
[12] SRINIVAS A, LIN T Y, PARMAR N, et al. Bottleneck transformers for visual recognition[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA: IEEE, 2021: 16519-16529.
[13] CAO K, RONG Y, LI C, et al. Pose-robust face recognition via deep residual equivariant mapping[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 5187-5196.
[14] WANG Y, GONG D, ZHOU Z, et al. Orthogonal deep features decomposition for age-invariant face recognition[C]//Proceedings of the European conference on computer vision (ECCV). Munich, Germany: Springer, Cham, 2018: 738-753.
[16] DENG J, GUO J, XUE N, et al. Arcface: additive angular margin loss for deep face recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019: 4690-4699.
[17] WANG H, WANG Y, ZHOU Z, et al. Cosface: large margin cosine loss for deep face recognition[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 5265-5274.
[18] LIU Cheng, CAO Liangcai, JING Ye, et al. Transformer for age-invariant face recognition[J]. Laser & Optoelectronics Progress, 2022, 60(10): 1-11.
[19] MOSCHOGLOU S, PAPAIOANNOU A, SAGONAS C, et al. Agedb: the first manually collected, in-the-wild age database[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, HI, USA: IEEE, 2017: 51-59.
[21] ZHENG T, DENG W, HU J. Cross-age lfw: a database for studying cross-age face recognition in unconstrained environments[EB/OL]. [2022-08-22]. https://arxiv.org/pdf/1708.08197.pdf.
[22] HUANG G B, MATTAR M, BERG T, et al. Labeled faces in the wild: a database forstudying face recognition in unconstrained environments[C]//Workshop on faces in'Real-Life'Images: detection, alignment, and recognition. Marseille, France: University of Massachusetts, Amherst, 2008: 7-49.
[23] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015: 1-9.