{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:17:44Z","timestamp":1768994264509,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556274","type":"print"},{"value":"9789819556281","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5628-1_4","type":"book-chapter","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:30:06Z","timestamp":1768944606000},"page":"47-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parsing-Induced Mixture-of-Experts for\u00a0Facial Age Estimation"],"prefix":"10.1007","author":[{"given":"Wen","family":"Liu","sequence":"first","affiliation":[]},{"given":"Xiaomei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Hongsen","family":"Bi","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Lei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"issue":"1","key":"4_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13640-018-0278-6","volume":"2018","author":"R Angulu","year":"2018","unstructured":"Angulu, R., Tapamo, J.R., Adewumi, A.O.: Age estimation via face images: a survey. EURASIP J. Image Video Proc. 2018(1), 1\u201335 (2018). https:\/\/doi.org\/10.1186\/s13640-018-0278-6","journal-title":"EURASIP J. Image Video Proc."},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.neucom.2023.02.037","volume":"534","author":"Z Bao","year":"2023","unstructured":"Bao, Z., et al.: Deep domain-invariant learning for facial age estimation. Neurocomputing 534, 86\u201393 (2023)","journal-title":"Neurocomputing"},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TIFS.2022.3218431","volume":"18","author":"Z Bao","year":"2023","unstructured":"Bao, Z., et al.: Divergence-driven consistency training for semi-supervised facial age estimation. IEEE Trans. Inf. Forensics Secur. 18, 221\u2013232 (2023)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Chen, P., et al.: DAA: a delta age adain operation for age estimation via binary code transformer. IN: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15836\u201315845 (2023)","DOI":"10.1109\/CVPR52729.2023.01520"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Chen, P., et al.: Learning triangular distribution in visual world. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11019\u201311029 (2024)","DOI":"10.1109\/CVPR52733.2024.01048"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical automated data augmentation with a reduced search space. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3008\u20133017 (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248\u2013255. Ieee (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Escalera, S., et al.: Chalearn looking at people 2015: apparent age and cultural event recognition datasets and results. In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 243\u2013251 (2015)","DOI":"10.1109\/ICCVW.2015.40"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"K\u00e4rkk\u00e4inen, K., Joo, J.: Fairface: face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1547\u20131557 (2021)","DOI":"10.1109\/WACV48630.2021.00159"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 34\u201342 (2015)","DOI":"10.1109\/CVPRW.2015.7301352"},{"key":"4_CR12","unstructured":"Li, P., Hu, Y., He, R., Sun, Z.: A coupled evolutionary network for age estimation. arXiv preprint arXiv:1809.07447 (2018)"},{"key":"4_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107178","volume":"100","author":"P Li","year":"2020","unstructured":"Li, P., Hu, Y., Wu, X., He, R., Sun, Z.: Deep label refinement for age estimation. Pattern Recogn. 100, 107178 (2020)","journal-title":"Pattern Recogn."},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Li, W., et al.: Bridgenet: a continuity-aware probabilistic network for age estimation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1145\u20131154 (2019)","DOI":"10.1109\/CVPR.2019.00124"},{"issue":"2","key":"4_CR15","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1109\/TCSVT.2017.2782709","volume":"29","author":"H Liu","year":"2019","unstructured":"Liu, H., Lu, J., Feng, J., Zhou, J.: Ordinal deep learning for facial age estimation. IEEE Trans. Circuits Syst. Video Technol. 29(2), 486\u2013501 (2019)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Liu, S., Shi, J., Ji, L., Yang, M.: Face parsing via recurrent propagation. In: 2017 Conference on British Machine Vision Conference (BMVC). BMVA Press (2017)","DOI":"10.5244\/C.31.8"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: Agenet: deeply learned regressor and classifier for robust apparent age estimation. In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 258\u2013266 (2015)","DOI":"10.1109\/ICCVW.2015.42"},{"key":"4_CR18","doi-asserted-by":"publisher","first-page":"17103","DOI":"10.1109\/ACCESS.2020.2967800","volume":"8","author":"SH Nam","year":"2020","unstructured":"Nam, S.H., Kim, Y.H., Truong, N.Q., Choi, J., Park, K.R.: Age estimation by super-resolution reconstruction based on adversarial networks. IEEE Access 8, 17103\u201317120 (2020)","journal-title":"IEEE Access"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output CNN for age estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4920\u20134928 (2016)","DOI":"10.1109\/CVPR.2016.532"},{"key":"4_CR20","unstructured":"Puigcerver, J., Riquelme, C., Mustafa, B., Houlsby, N.: From sparse to soft mixtures of experts. In: Thirty-Fourth AAAI Conference on Artificial Intelligence. vol. abs\/2308.00951 (2023)"},{"key":"4_CR21","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.1109\/TCSVT.2023.3304724","volume":"34","author":"L Qin","year":"2023","unstructured":"Qin, L., et al.: Swinface: a multi-task transformer for face recognition, expression recognition, age estimation and attribute estimation. IEEE Trans. Circuits Syst. Video Technol. 34, 2223\u20132234 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Ricanek, K., Tesafaye, T.: Morph: a longitudinal image database of normal adult age-progression. In: 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp. 341\u2013345 (2006)","DOI":"10.1109\/FGR.2006.78"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Rothe, R., et\u00a0al.: Dex: deep expectation of apparent age from a single image. In: 2016 IEEE International Conference on Computer Vision (ICCV) (2016)","DOI":"10.1109\/ICCVW.2015.41"},{"key":"4_CR24","unstructured":"Shazeer, N., et al.: Outrageously large neural networks: the sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017)"},{"issue":"2","key":"4_CR25","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1109\/TPAMI.2019.2937294","volume":"43","author":"W Shen","year":"2021","unstructured":"Shen, W., et al.: Deep differentiable random forests for age estimation. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 404\u2013419 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Shin, N.H., Lee, S.H., Kim, C.S.: Moving window regression: A novel approach to ordinal regression. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18739\u201318748. IEEE Computer Society, Los Alamitos, CA, USA (Jun 2022). 10.1109\/CVPR52688.2022.01820","DOI":"10.1109\/CVPR52688.2022.01820"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Shu, x., Tang, J., Li, Z., Lai, H., Zhang, L., Yan, S.: Personalized age progression with bi-level aging dictionary learning. IEEE Trans. Patt. Anal. Mach. Intell. 40(4), 905\u2013917 (2018)","DOI":"10.1109\/TPAMI.2017.2705122"},{"key":"4_CR28","doi-asserted-by":"publisher","first-page":"4679","DOI":"10.1109\/TIFS.2021.3114066","volume":"16","author":"H Sun","year":"2021","unstructured":"Sun, H., Pan, H., Han, H., Shan, S.: Deep conditional distribution learning for age estimation. IEEE Trans. Inf. Forensics Secur. 16, 4679\u20134690 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"4_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114786","volume":"175","author":"RK Tripathi","year":"2021","unstructured":"Tripathi, R.K., Jalal, A.S.: Novel local feature extraction for age invariant face recognition. Expert Syst. Appl. 175, 114786 (2021)","journal-title":"Expert Syst. Appl."},{"key":"4_CR30","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-981-99-8543-2_13","volume-title":"Pattern Recognition and Computer Vision","author":"T Wang","year":"2024","unstructured":"Wang, T., Dong, X., Li, Z., Liu, H.: Co-regularized facial age estimation with graph-causal learning. In: Liu, Q., et al. (eds.) Pattern Recognition and Computer Vision, pp. 155\u2013166. Springer Nature Singapore, Singapore (2024)"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Yang, T.Y., Huang, Y.H., Lin, Y.Y., Hsiu, P.C., Chuang, Y.Y.: SSR-net: a compact soft stagewise regression network for age estimation. In: 2018 International Joint Conference on Artificial Intelligence (IJCAI). vol.\u00a05, p.\u00a07 (2018)","DOI":"10.24963\/ijcai.2018\/150"},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, L., Li, C., Loy, C.C.: Quantifying facial age by posterior of age comparisons. In: British Machine Vision Conference (BMVC) (2017)","DOI":"10.5244\/C.31.108"},{"issue":"7","key":"4_CR33","doi-asserted-by":"publisher","first-page":"3108","DOI":"10.1109\/TNNLS.2020.3009523","volume":"32","author":"Q Zhao","year":"2021","unstructured":"Zhao, Q., Dong, J., Yu, H., Chen, S.: Distilling ordinal relation and dark knowledge for facial age estimation. IEEE Trans. Neural Netw. Learn. Syst. 32(7), 3108\u20133121 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"4_CR34","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Deng, J., Zhu, Z.H., Li, Y., Zafeiriou, S.: Decoupled multi-task learning with cyclical self-regulation for face parsing. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4146\u20134155 (2022)","DOI":"10.1109\/CVPR52688.2022.00412"},{"key":"4_CR35","doi-asserted-by":"crossref","unstructured":"Zheng, Y., et al.: General facial representation learning in a visual-linguistic manner. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18676\u201318688 (2021)","DOI":"10.1109\/CVPR52688.2022.01814"},{"key":"4_CR36","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Thirty-Fourth AAAI Conference on Artificial Intelligence. vol.\u00a034, pp. 13001\u201313008 (2020)","DOI":"10.1609\/aaai.v34i07.7000"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5628-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:30:10Z","timestamp":1768944610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5628-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556274","9789819556281"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5628-1_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"21 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}