{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:29:20Z","timestamp":1773156560800,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003725","name":"national research foundation of korea","doi-asserted-by":"publisher","award":["GR 2019R1D1A3A03103736"],"award-info":[{"award-number":["GR 2019R1D1A3A03103736"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-07176-7","type":"journal-article","created":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T14:03:42Z","timestamp":1648476222000},"page":"13063-13074","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning age semantic factor to enhance group-based representations for cross-age face recognition"],"prefix":"10.1007","volume":"34","author":[{"given":"Chenmou","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2581-5268","authenticated-orcid":false,"given":"Hyo Jong","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"issue":"4","key":"7176_CR1","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1049\/iet-cvi.2014.0084","volume":"9","author":"M Hassaballah","year":"2015","unstructured":"Hassaballah M, Aly S (2015) Face recognition: challenges, achievements and future directions. IET Comput Vis J 9(4):614\u2013626","journal-title":"IET Comput Vis J"},{"key":"7176_CR2","doi-asserted-by":"crossref","unstructured":"AlWaisy AS, AlFahdawi S, Qahwaji R (2020) A multi-biometric face recognition system based on multimodal deep learning representations. Deep Learn Comput Vis 89\u2013126","DOI":"10.1201\/9781351003827-4"},{"issue":"2","key":"7176_CR3","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1007\/s11042-017-4359-9","volume":"77","author":"Y Gao","year":"2018","unstructured":"Gao Y, Lee HJ (2018) Learning warps based similarity for pose-unconstrained face recognition. Multimed Tools Appl 77(2):1927\u20131942","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"7176_CR4","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1504\/IJBM.2010.030415","volume":"2","author":"A Lanitis","year":"2010","unstructured":"Lanitis A (2010) A survey of the effects of aging on biometric identity verification. Int J Biometrics 2(1):34\u201352","journal-title":"Int J Biometrics"},{"key":"7176_CR5","doi-asserted-by":"crossref","unstructured":"Wang Y, Gong D, Zhou Z, Ji X, Wang H, Li Z, Liu W, Zhang T (2018) Orthogonal deep features decomposition for age-invariant face recognition. In: Proceedings of the European conference on computer vision (ECCV), pp 738\u2013753","DOI":"10.1007\/978-3-030-01267-0_45"},{"key":"7176_CR6","doi-asserted-by":"crossref","unstructured":"Wang H, Gong D, Li Z, Liu W (2019) Decorrelated adversarial learning for age-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3527\u20133536","DOI":"10.1109\/CVPR.2019.00364"},{"key":"7176_CR7","doi-asserted-by":"crossref","unstructured":"Gong D, Li Z, Lin D, Liu J, Tang X (2013) Hidden factor analysis for age invariant face recognition. In: Proceedings of international conference on computer vision (ICCV), pp 2872\u20132879","DOI":"10.1109\/ICCV.2013.357"},{"key":"7176_CR8","doi-asserted-by":"crossref","unstructured":"Wen Y, Li Z, Qiao Y (2016) Latent factor guided convolutional neural networks for age-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 4893\u20134901","DOI":"10.1109\/CVPR.2016.529"},{"key":"7176_CR9","doi-asserted-by":"crossref","unstructured":"Zheng T, Deng W, Hu J (2017) Age estimation guided convolutional neural network for age-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 12\u201316","DOI":"10.1109\/CVPRW.2017.77"},{"key":"7176_CR10","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al (2014) Generative adversarial networks. In: Proceedings of the international conference on neural information processing systems (NIPS), pp 2672\u20132680"},{"key":"7176_CR11","unstructured":"Zhao J, Yan S, Feng J (2020) Towards age-invariant face recognition. In: IEEE transactions on pattern analysis and machine intelligence (PAMI)"},{"key":"7176_CR12","doi-asserted-by":"crossref","unstructured":"Wang W, Cui Z, Yan Y, Feng J, Yan S, Shu X, Sebe N (2016) Recurrent face aging. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp f78\u20132386","DOI":"10.1109\/CVPR.2016.261"},{"key":"7176_CR13","doi-asserted-by":"crossref","unstructured":"Zhang Z, Song Y, Qi H (2017) Age progression\/regression by conditional adversarial autoencoder. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, USA, pp 4352\u20134360","DOI":"10.1109\/CVPR.2017.463"},{"key":"7176_CR14","doi-asserted-by":"crossref","unstructured":"Song J, Zhang J, Gao L, Liu X, Shen HT (2018) Dual conditional GANs for face aging and rejuvenation. In: IJCAI, pp 899\u2013905","DOI":"10.24963\/ijcai.2018\/125"},{"key":"7176_CR15","unstructured":"Fg-net aging database (2007) http:\/\/webmail.cycollege.ac.cy\/alanitis\/fgnetaging\/"},{"key":"7176_CR16","doi-asserted-by":"crossref","unstructured":"Moschoglou S, Papaioannou A, Sagonas C, Deng J, Kotsia I, Zafeiriou S (2017) Agedb: the first manually collected, in-the-wild age database. In: CVPRW, pp 1997\u20132005","DOI":"10.1109\/CVPRW.2017.250"},{"key":"7176_CR17","doi-asserted-by":"crossref","unstructured":"Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) SphereFace: deep hypersphere embedding for face recognition. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6738\u20136746","DOI":"10.1109\/CVPR.2017.713"},{"key":"7176_CR18","doi-asserted-by":"crossref","unstructured":"Ricanek K, Tesafaye T (2006) Morph: a longitudinal image database of normal adult age-progression. In: FGR, pp 341\u2013345","DOI":"10.1109\/FGR.2006.78"},{"issue":"6","key":"7176_CR19","first-page":"804","volume":"17","author":"B-C Chen","year":"2015","unstructured":"Chen B-C, Chen C-S, Hsu WH (2015) Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset. TMM 17(6):804\u2013815","journal-title":"TMM"},{"key":"7176_CR20","unstructured":"Kingma D-P, Welling M (2014) Auto-encoding variational Bayes. In: International conference on learning representations (ICLR)"},{"issue":"11","key":"7176_CR21","first-page":"3349","volume":"15","author":"N Ramanathan","year":"2006","unstructured":"Ramanathan N, Chellappa R (2006) Face verification across age progression. T-IP 15(11):3349\u20133361","journal-title":"T-IP"},{"key":"7176_CR22","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. JMLR 10:207\u2013244","journal-title":"JMLR"},{"issue":"1","key":"7176_CR23","first-page":"82","volume":"5","author":"H Ling","year":"2010","unstructured":"Ling H, Soatto S, Ramanathan N, Jacobs DW (2010) Face verification across age progression using discriminative methods. T-IFS 5(1):82\u201391","journal-title":"T-IFS"},{"key":"7176_CR24","doi-asserted-by":"crossref","unstructured":"Zheng T, Deng W, Hu J (2017) Age estimation guided convolutional neural network for age-invariant face recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 12\u201316","DOI":"10.1109\/CVPRW.2017.77"},{"key":"7176_CR25","doi-asserted-by":"crossref","unstructured":"Cheng Y, Zhao J, Wang Z, Xu Y, Karlekar J, Shen S, Feng J (2017) Know you at one glance: a compact vector representation for low-shot learning. In: ICCVW, pp 1924\u20131932","DOI":"10.1109\/ICCVW.2017.227"},{"issue":"9","key":"7176_CR26","doi-asserted-by":"publisher","first-page":"3482","DOI":"10.1109\/TCSVT.2020.3040296","volume":"31","author":"Y Wu","year":"2021","unstructured":"Wu Y, Du L, Hu H (2021) Parallel multi-path age distinguish network for cross-age face recognition. IEEE Trans Circuits Syst Video Technol 31(9):3482\u20133492","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"7","key":"7176_CR27","doi-asserted-by":"publisher","first-page":"2675","DOI":"10.1109\/TCSVT.2020.3024766","volume":"31","author":"L Du","year":"2021","unstructured":"Du L, Hu H (2021) Cross-age identity difference analysis model based on image pairs for age invariant face verification. IEEE Trans Circuits Syst Video Technol 31(7):2675\u20132685","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"7176_CR28","doi-asserted-by":"crossref","unstructured":"Huang Z, Zhang J, Shan H (2021) When age-invariant face recognition meets face age synthesis: a multi-task learning framework. arXiv preprint arXiv:2103.01520","DOI":"10.1109\/CVPR46437.2021.00720"},{"key":"7176_CR29","unstructured":"Higgins I, Matthey L, Pal A, Burgess C, Glorot X, Botvinick M, Mohamed S, Lerchner A (2017) beta-vae: Learning basic visual concepts with a constrained variational framework. In: International conference on learning representations (ICCV)"},{"key":"7176_CR30","unstructured":"Chen RTQ, Li X, Grosse R, Duvenaud D. Isolating sources of disentanglement in VAEs. In: Proceedings of the 32nd international conference on neural information processing systems (NIPS\u201918). Curran Associates Inc., Red Hook, pp 2615\u20132625"},{"key":"7176_CR31","unstructured":"Oord AVD, Vinyals O, Koray KK (2017) Neural discrete representation learning. In: Proceedings of the 31st international conference on neural information processing systems (NIPS\u201917). Curran Associates Inc, Red Hook, pp 6309\u20136318"},{"key":"7176_CR32","unstructured":"Razavi A, van den Oord A, Vinyals O (2019) Generating diverse high-fidelity images with VQ-VAE-2. arXiv e-prints"},{"key":"7176_CR33","doi-asserted-by":"crossref","unstructured":"Kim Y, Park W, Roh M-C, Shin J (2020) GroupFace: learning latent groups and constructing group-based representations for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 5621\u20135630","DOI":"10.1109\/CVPR42600.2020.00566"},{"key":"7176_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition.. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"7176_CR35","unstructured":"Doersch C (2016) Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908"},{"key":"7176_CR36","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"7176_CR37","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Niannan X, Zafeiriou S (2019) Arcface: additive angular margin loss for deep face recognition. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"7176_CR38","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: International conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2015.425"},{"key":"7176_CR39","unstructured":"Yi D, Lei Z, Liao S, Li SZ (2014) Learning face representation from scratch (2014). arXiv preprint arXiv:1411.7923"},{"key":"7176_CR40","doi-asserted-by":"crossref","unstructured":"Moschoglou S, Papaioannou A, Sagonas C, Deng J, Kotsia I, Zafeiriou S (2017) Agedb: the first manually collected, in-the-wild age database. In: Proceedings of IEEE conference on computer vision and pattern recognition workshops (CVPRW)","DOI":"10.1109\/CVPRW.2017.250"},{"key":"7176_CR41","doi-asserted-by":"crossref","unstructured":"Rothe R, Timofte R, Gool LV (2015) Dex: deep expectation of apparent age from a single image. In: Proceedings of international conference on computer vision workshops (ICCVW)","DOI":"10.1109\/ICCVW.2015.41"},{"issue":"10","key":"7176_CR42","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multi-task cascaded convolutional networks. Signal Process Lett 23(10):1499\u20131503","journal-title":"Signal Process Lett"},{"key":"7176_CR43","unstructured":"Paszke A et al (2019) Pytorch: an imperative style high-performance deep learning library. In: Proc. Adv. Neural Inf. Process. Syst., pp 8026\u20138037"},{"key":"7176_CR44","doi-asserted-by":"crossref","unstructured":"Smith LN (2017) Cyclical learning rates for training neural networks. 2017 IEEE winter conference on applications of computer vision (WACV). Santa Rosa, CA, USA, pp 464\u2013472","DOI":"10.1109\/WACV.2017.58"},{"key":"7176_CR45","unstructured":"Glorot X, Bengio Y (2015) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR workshop and conference proceedings, pp 249\u2013256"},{"key":"7176_CR46","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: ICCV","DOI":"10.1109\/ICCV.2015.123"},{"key":"7176_CR47","doi-asserted-by":"crossref","unstructured":"Chen BC, Chen CS, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-319-10599-4_49"},{"issue":"3","key":"7176_CR48","first-page":"1028","volume":"6","author":"Z Li","year":"2011","unstructured":"Li Z, Park U, Jain AK (2011) A discriminative model for age invariant face recognition. T-IFS 6(3):1028\u20131037","journal-title":"T-IFS"},{"key":"7176_CR49","first-page":"499","volume-title":"European conference on computer vision","author":"Y Wen","year":"2016","unstructured":"Wen Y, Zhang K, Li Z et al (2016) A discriminative feature learning approach for deep face recognition. European conference on computer vision. Springer, Cham, pp 499\u2013515"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07176-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07176-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07176-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T10:18:18Z","timestamp":1658571498000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07176-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,28]]},"references-count":49,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7176"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07176-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,28]]},"assertion":[{"value":"24 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}