• Laser & Optoelectronics Progress
  • Vol. 59, Issue 2, 0215001 (2022)
Wenbin Sun1, Rong Wang2、3、*, Lianzhu Sun4, and Yuansong Lin1
Author Affiliations
  • 1School of Information Engineering, Guangdong University of Technology, Guangzhou , Guangdong 510006, China
  • 2College of Information Engineering, Northwest A&F University, Xianyang , Shaanxi 712100, China
  • 3National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
  • 4School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
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    DOI: 10.3788/LOP202259.0215001 Cite this Article Set citation alerts
    Wenbin Sun, Rong Wang, Lianzhu Sun, Yuansong Lin. Cross-Age Face Recognition Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0215001 Copy Citation Text show less
    References

    [1] Chen B C, Chen C S, Hsu W H. Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset[J]. IEEE Transactions on Multimedia, 17, 804-815(2015).

    [2] Best-Rowden L, Jain A K. Longitudinal study of automatic face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 148-162(2018).

    [3] Sawant M M, Bhurchandi K M. Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging[J]. Artificial Intelligence Review, 52, 981-1008(2019).

    [4] Agrawal A K, Singh Y N. Evaluation of face recognition methods in unconstrained environments[J]. Procedia Computer Science, 48, 644-651(2015).

    [5] Khiyari H, Wechsler H. Age invariant face recognition using convolutional neural networks and set distances[J]. Journal of Information Security, 8, 174-185(2017).

    [6] Mortezaie Z, Hassanpour H. A survey on age invariant face recognition methods[J]. Jordanian Journal of Computers and Information Technology, 5, 87-96(2019).

    [7] Wang M, Deng W H. Deep face recognition: a survey[EB/OL]. https:?∥arxiv.org/abs/1804.06655

    [8] Lei Z, Zhu P F, Hu Q H et al. A linear subspace learning approach via sparse coding[C], 755-761(2011).

    [9] Li Z F, Park U, Jain A K. A discriminative model for age invariant face recognition[J]. IEEE Transactions on Information Forensics and Security, 6, 1028-1037(2011).

    [10] Gong D H, Li Z F, Lin D H et al. Hidden factor analysis for age invariant face recognition[C], 2872-2879(2013).

    [11] Gong D H, Li Z F, Tao D C et al. A maximum entropy feature descriptor for age invariant face recognition[C], 5289-5297(2015).

    [12] Wen Y D, Li Z F, Qiao Y. Latent factor guided convolutional neural networks for age-invariant face recognition[C], 4893-4901(2016).

    [13] Xu C F, Liu Q H, Ye M. Age invariant face recognition and retrieval by coupled auto-encoder networks[J]. Neurocomputing, 222, 62-71(2017).

    [14] Zheng T Y, Deng W H, Hu J N. Age estimation guided convolutional neural network for age-invariant face recognition[C], 503-511(2017).

    [15] Wang Y T, Gong D H, Zhou Z et al. Orthogonal deep features decomposition for age-invariant face recognition[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11219, 764-779(2018).

    [16] Wang H, Gong D H, Li Z F et al. Decorrelated adversarial learning for age-invariant face recognition[C], 3522-3531(2019).

    [17] Hotelling H. Relations between two sets of variates[J]. Biometrika, 28, 321-377(1936).

    [18] Yu J H, Lin Z, Yang J M et al. Free-form image inpainting with gated convolution[C], 4470-4479(2019).

    [19] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

    [20] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https:?∥arxiv.org/abs/1409.1556

    [21] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C], 1-9(2015).

    [22] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [23] Deng J K, Guo J, Yang J et al. ArcFace: additive angular margin loss for deep face recognition[C], 4685-4694(2019).

    [24] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL]. https:?∥arxiv.org/abs/1502.03167

    [25] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C], 1026-1034(2015).

    [26] Yu J B, Jing L P. A joint multi-task CNN for cross-age face recognition[C], 2411-2415(2018).

    [27] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

    [28] Wang Q L, Wu B G, Zhu P F et al. ECA-net: Efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

    [29] Akaho S. A kernel method for canonical correlation analysis[EB/OL]. https:∥rxiv.org/abs/cs/0609071

    [30] Wang H, Wang Y T, Zhou Z et al. CosFace: large margin cosine loss for deep face recognition[C], 5265-5274(2018).

    [31] Zhang K P, Zhang Z P, Li Z F et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 23, 1499-1503(2016).

    [32] Guo Q Y. Research on cross-age face recognition based on age-invariant identity feature[D], 37-38(2020).

    [33] Zheng T Y, Deng W H, Hu J N. Cross-age LFW: a database for studying cross-age face recognition in unconstrained environments[EB/OL]. https:∥arxiv.org/abs/1708.08197

    [34] Li H X, Hu H F, Yip C. Age-related factor guided joint task modeling convolutional neural network for cross-age face recognition[J]. IEEE Transactions on Information Forensics and Security, 13, 2383-2392(2018).

    [35] Parkhi O M, Vedaldi A, Zisserman A. Deep face recognition[C], 41.1-41.12(2015).

    [36] Chen B H, Deng W H, Du J P. Noisy softmax: improving the generalization ability of DCNN via postponing the early softmax saturation[C], 4021-4030(2017).

    Wenbin Sun, Rong Wang, Lianzhu Sun, Yuansong Lin. Cross-Age Face Recognition Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0215001
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