{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T04:21:24Z","timestamp":1743913284754,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030337193"},{"type":"electronic","value":"9783030337209"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33720-9_14","type":"book-chapter","created":{"date-parts":[[2019,12,18]],"date-time":"2019-12-18T10:48:15Z","timestamp":1576666095000},"page":"181-193","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Skin Identification Using Deep Convolutional Neural Network"],"prefix":"10.1007","author":[{"given":"Mahdi Maktab Dar","family":"Oghaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasileios","family":"Argyriou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dorothy","family":"Monekosso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Remagnino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,21]]},"reference":[{"key":"14_CR1","unstructured":"Bae, H.J., Jung, S.H.: Image retrieval using texture based on DCT. In: Proceedings of 1997 International Conference on Information, Communications and Signal Processing, ICICS 1997, vol. 2, pp. 1065\u20131068. IEEE (1997)"},{"issue":"4","key":"14_CR2","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1111\/j.1365-2133.2010.09639.x","volume":"162","author":"PR Bargo","year":"2010","unstructured":"Bargo, P.R., Kollias, N.: Measurement of skin texture through polarization imaging. Br. J. Dermatol. 162(4), 724\u2013731 (2010)","journal-title":"Br. J. Dermatol."},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Chaichulee, S., et al.: Multi-task convolutional neural network for patient detection and skin segmentation in continuous non-contact vital sign monitoring. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 266\u2013272. IEEE (2017)","DOI":"10.1109\/FG.2017.41"},{"key":"14_CR4","unstructured":"Du, S.S., Zhai, X., Poczos, B., Singh, A.: Gradient descent provably optimizes over-parameterized neural networks. arXiv preprint arXiv:1810.02054 (2018)"},{"issue":"7639","key":"14_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)","journal-title":"Nature"},{"issue":"18","key":"14_CR6","doi-asserted-by":"publisher","first-page":"13225","DOI":"10.1016\/j.eswa.2012.05.079","volume":"39","author":"WY Han","year":"2012","unstructured":"Han, W.Y., Lee, J.C.: Palm vein recognition using adaptive Gabor filter. Expert Syst. Appl. 39(18), 13225\u201313234 (2012)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"14_CR7","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/PROC.1979.11328","volume":"67","author":"RM Haralick","year":"1979","unstructured":"Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786\u2013804 (1979)","journal-title":"Proc. IEEE"},{"key":"14_CR8","unstructured":"Lloyd, K., Marshall, D., Moore, S.C., Rosin, P.L.: Detecting violent crowds using temporal analysis of GLCM texture. arXiv preprint arXiv 1605 (2016)"},{"key":"14_CR9","unstructured":"Mahmoud, M.K.A., Al-Jumaily, A.: A hybrid system for skin lesion detection: based on Gabor wavelet and support vector machine. In: Information Technology: Proceedings of the 2014 International Symposium on Information Technology (ISIT 2014), Dalian, China, 14\u201316 October 2014, p. 39. CRC Press (2015)"},{"issue":"6","key":"14_CR10","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1007\/s00521-017-3164-8","volume":"31","author":"MM Oghaz","year":"2019","unstructured":"Oghaz, M.M., Maarof, M.A., Rohani, M.F., Zainal, A., Shaid, S.Z.M.: An optimized skin texture model using gray-level co-occurrence matrix. Neural Comput. Appl. 31(6), 1835\u20131853 (2019)","journal-title":"Neural Comput. Appl."},{"issue":"8","key":"14_CR11","doi-asserted-by":"publisher","first-page":"e0134828","DOI":"10.1371\/journal.pone.0134828","volume":"10","author":"MM Oghaz","year":"2015","unstructured":"Oghaz, M.M., Maarof, M.A., Zainal, A., Rohani, M.F., Yaghoubyan, S.H.: A hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique. PLoS ONE 10(8), e0134828 (2015)","journal-title":"PLoS ONE"},{"issue":"3","key":"14_CR12","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1016\/j.sjbs.2017.01.021","volume":"24","author":"H Pang","year":"2017","unstructured":"Pang, H., Chen, T., Wang, X., Chang, Z., Shao, S., Zhao, J.: Quantitative evaluation methods of skin condition based on texture feature parameters. Saudi J. Biol. Sci. 24(3), 514\u2013518 (2017)","journal-title":"Saudi J. Biol. Sci."},{"issue":"1","key":"14_CR13","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TPAMI.2005.17","volume":"27","author":"SL Phung","year":"2005","unstructured":"Phung, S.L., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148\u2013154 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Raupov, D.S., Myakinin, O.O., Bratchenko, I.A., Zakharov, V.P., Khramov, A.G.: Skin cancer texture analysis of OCT images based on Haralick, fractal dimension, Markov random field features, and the complex directional field features. In: Optics in Health Care and Biomedical Optics VII, vol. 10024, p. 100244I. International Society for Optics and Photonics (2016)","DOI":"10.1117\/12.2246446"},{"key":"14_CR15","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1016\/j.neucom.2015.04.119","volume":"175","author":"A Rubel","year":"2016","unstructured":"Rubel, A., Lukin, V., Uss, M., Vozel, B., Pogrebnyak, O., Egiazarian, K.: Efficiency of texture image enhancement by DCT-based filtering. Neurocomputing 175, 948\u2013965 (2016)","journal-title":"Neurocomputing"},{"key":"14_CR16","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Sun, Y., Tistarelli, M., Maltoni, D.: Structural similarity based image quality map for face recognition across plastic surgery. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1\u20138. IEEE (2013)","DOI":"10.1109\/BTAS.2013.6712737"},{"issue":"3","key":"14_CR18","first-page":"93","volume":"3","author":"F Torkashvand","year":"2015","unstructured":"Torkashvand, F., Fartash, M.: Automatic segmentation of skin lesion using Markov random field. Can. J. Basic Appl. Sci. 3(3), 93\u2013107 (2015)","journal-title":"Can. J. Basic Appl. Sci."},{"issue":"6","key":"14_CR19","first-page":"27","volume":"2","author":"SH Yaghoubyan","year":"2015","unstructured":"Yaghoubyan, S.H., Maarof, M.A., Zainal, A., Fo\u00e2, M., Oghaz, M.M., et al.: Fast and effective bag-of-visual-word model to pornographic images recognition using the freak descriptor. J. Soft Comput. Decis. Support Syst. 2(6), 27\u201333 (2015)","journal-title":"J. Soft Comput. Decis. Support Syst."},{"issue":"9","key":"14_CR20","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1109\/TMI.2017.2695227","volume":"36","author":"Y Yuan","year":"2017","unstructured":"Yuan, Y., Chao, M., Lo, Y.C.: Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE Trans. Med. Imaging 36(9), 1876\u20131886 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Weng, C., Yu, B., Li, H.: In-vivo differentiation of photo-aged epidermis skin by texture-based classification. In: Optics in Health Care and Biomedical Optics VI, vol. 9268, p. 92682G. International Society for Optics and Photonics (2014)","DOI":"10.1117\/12.2072011"},{"issue":"3","key":"14_CR22","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/LSP.2017.2654803","volume":"24","author":"H Zuo","year":"2017","unstructured":"Zuo, H., Fan, H., Blasch, E., Ling, H.: Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Process. Lett. 24(3), 289\u2013293 (2017)","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33720-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T21:55:46Z","timestamp":1743890146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33720-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030337193","9783030337209"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33720-9_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"21 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lake Tahoe, NV","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isvc.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"163","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}