{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:00:19Z","timestamp":1763564419165,"version":"3.41.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031915680","type":"print"},{"value":"9783031915697","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-91569-7_22","type":"book-chapter","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:49:54Z","timestamp":1748090994000},"page":"352-367","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Armocromia: A Novel Dataset for\u00a0Face Seasonal Color Analysis and\u00a0Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9341-7651","authenticated-orcid":false,"given":"Lorenzo","family":"Stacchio","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5523-7174","authenticated-orcid":false,"given":"Marina","family":"Paolanti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7618-4948","authenticated-orcid":false,"given":"Francesca","family":"Spigarelli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-9244","authenticated-orcid":false,"given":"Emanuele","family":"Frontoni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Bae, G., et al.: DigiFace-1m: 1 million digital face images for face recognition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3526\u20133535 (2023)","DOI":"10.1109\/WACV56688.2023.00352"},{"key":"22_CR2","unstructured":"Bonandini, E.: Smart beauty essential. EIFIS Editore (2021)"},{"key":"22_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1007\/978-3-030-01449-0_37","volume-title":"Advanced Concepts for Intelligent Vision Systems","author":"D Borza","year":"2018","unstructured":"Borza, D., Ileni, T., Darabant, A.: A deep learning approach to hair segmentation and color extraction from facial images. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2018. LNCS, vol. 11182, pp. 438\u2013449. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01449-0_37"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, P., Zhou, J.: Automatic season classification of outdoor photos. In: 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics, vol.\u00a01, pp. 46\u201349. IEEE (2011)","DOI":"10.1109\/IHMSC.2011.18"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Choi, J.Y., Ro, Y.M., Plataniotis, K.N.: Color face recognition for degraded face images. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 39(5), 1217\u20131230 (2009)","DOI":"10.1109\/TSMCB.2009.2014245"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Farid, H.: An overview of perceptual hashing. J. Online Trust Saf. 1(1) (2021)","DOI":"10.54501\/jots.v1i1.24"},{"issue":"12","key":"22_CR7","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1109\/TPAMI.2014.2321570","volume":"36","author":"S Fu","year":"2014","unstructured":"Fu, S., He, H., Hou, Z.G.: Learning race from face: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2483\u20132509 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR8","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press (2016)"},{"issue":"5","key":"22_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3527850","volume":"55","author":"A Kammoun","year":"2022","unstructured":"Kammoun, A., Slama, R., Tabia, H., Ouni, T., Abid, M.: Generative adversarial networks for face generation: a survey. ACM Comput. Surv. 55(5), 1\u201337 (2022)","journal-title":"ACM Comput. Surv."},{"key":"22_CR10","volume-title":"Color Me a Season: A Complete Guide to Finding Your Best Colors and How to Use Them","author":"B Kentner","year":"1978","unstructured":"Kentner, B.: Color Me a Season: A Complete Guide to Finding Your Best Colors and How to Use Them. Kenkra Publishers, Concord, Calif (1978)"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730\u20133738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"22_CR12","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"22_CR13","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Lou, Z., Gevers, T., Hu, N., Lucassen, M.P., et\u00a0al.: Color constancy by deep learning. In: BMVC pp. 76\u20131 (2015)","DOI":"10.5244\/C.29.76"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Mehdipour\u00a0Ghazi, M., Kemal\u00a0Ekenel, H.: A comprehensive analysis of deep learning based representation for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34\u201341 (2016)","DOI":"10.1109\/CVPRW.2016.20"},{"key":"22_CR16","unstructured":"Migliaccio, R.: Armocromia: il metodo dei colori amici che rivoluziona la vita e non solo l\u2019immagine. Vallardi (2019)"},{"key":"22_CR17","unstructured":"Migliaccio, R.: Italian image institute (2024). https:\/\/italianimageinstitute.it\/. Accessed 26 Aug 2024"},{"key":"22_CR18","unstructured":"Narayan, K., VS, V., Chellappa, R., Patel, V.M.: FacexFormer: a unified transformer for facial analysis. arXiv preprint arXiv:2403.12960 (2024)"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Rahmad, C., Arai, K., Asmara, R.A., Ekojono, E., Putra, D.R.: Comparison of geometric features and color features for face recognition. Int. J. Intell. Eng. Syst. 14(1) (2021)","DOI":"10.22266\/ijies2021.0228.50"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Ramirez, G.A., Fuentes, O., Crites\u00a0Jr, S.L., Jimenez, M., Ordonez, J.: Color analysis of facial skin: Detection of emotional state. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 468\u2013473 (2014)","DOI":"10.1109\/CVPRW.2014.76"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Rosati, R., Romeo, L., Vargas, V.M., Gutierrez, P.A., Frontoni, E., Hervas-Martinez, C.: Learning ordinal\u2013hierarchical constraints for deep learning classifiers. IEEE Trans. Neural Netw. Learn. Syst. 36, 4765\u20134778 (2024)","DOI":"10.1109\/TNNLS.2024.3360641"},{"key":"22_CR22","unstructured":"Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., Komatsuzaki, A.: Laion-400m: open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114 (2021)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Shahar, H., Hel-Or, H.: Micro expression classification using facial color and deep learning methods. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00207"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Stadelmann, T., Tolkachev, V., Sick, B., Stampfli, J., D\u00fcrr, O.: Beyond ImageNet: deep learning in industrial practice. In: Applied Data Science: Lessons Learned for the Data-Driven Business, pp. 205\u2013232 (2019)","DOI":"10.1007\/978-3-030-11821-1_12"},{"issue":"5","key":"22_CR25","doi-asserted-by":"publisher","first-page":"12597","DOI":"10.1007\/s11042-023-16014-4","volume":"83","author":"X Su","year":"2024","unstructured":"Su, X., et al.: Personalized clothing recommendation fusing the 4-season color system and users\u2019 biological characteristics. Multimedia Tools Appl. 83(5), 12597\u201312625 (2024)","journal-title":"Multimedia Tools Appl."},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891\u20131898 (2014)","DOI":"10.1109\/CVPR.2014.244"},{"key":"22_CR27","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.neucom.2020.10.081","volume":"429","author":"M Wang","year":"2021","unstructured":"Wang, M., Deng, W.: Deep face recognition: a survey. Neurocomputing 429, 215\u2013244 (2021)","journal-title":"Neurocomputing"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Webster, M.A., Mizokami, Y., Webster, S.M.: Seasonal variations in the color statistics of natural images. Netw. Comput. Neural Syst. 18(3), 213\u2013233 (2007)","DOI":"10.1080\/09548980701654405"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"issue":"6","key":"22_CR30","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","volume":"44","author":"M Ye","year":"2021","unstructured":"Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.: Deep learning for person re-identification: a survey and outlook. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 2872\u20132893 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Zheng, Y., et al.: General facial representation learning in a visual-linguistic manner. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18697\u201318709 (2022)","DOI":"10.1109\/CVPR52688.2022.01814"},{"issue":"2","key":"22_CR32","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1049\/ipr2.12366","volume":"16","author":"W Zhu","year":"2022","unstructured":"Zhu, W., Sang, P., He, Y.: Facial skin colour classification using machine learning and hyperspectral imaging data. IET Image Proc. 16(2), 509\u2013520 (2022)","journal-title":"IET Image Proc."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91569-7_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:50:04Z","timestamp":1748091004000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91569-7_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031915680","9783031915697"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91569-7_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}