{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:00:24Z","timestamp":1742929224925,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031067877"},{"type":"electronic","value":"9783031067884"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06788-4_6","type":"book-chapter","created":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T23:03:27Z","timestamp":1656889407000},"page":"62-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Non-contact Heart Rate Detection Based on Fusion Method of Visible Images and Infrared Images"],"prefix":"10.1007","author":[{"given":"Juncun","family":"Wei","sequence":"first","affiliation":[]},{"given":"Jiancheng","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Jiaxin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhengzheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"issue":"2","key":"6_CR1","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1152\/ajplegacy.1938.124.2.328","volume":"124","author":"AB Hertzman","year":"1938","unstructured":"Hertzman, A.B.: The blood supply of various skin areas as estimated by the photoelectric plethysmograph. Am. J. Physiol. 124(2), 328\u2013340 (1938)","journal-title":"Am. J. Physiol."},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.inffus.2017.10.007","volume":"42","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Chen, X., Wang, Z.J., Ward, R.K., Wang, X.: Deep learning for pixel-level image fusion: Recent advances and future prospects. Inf. Fusion 42, 158\u2013173 (2018)","journal-title":"Inf. Fusion"},{"issue":"3","key":"6_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/0967-3334\/28\/3\/R01","volume":"28","author":"J Allen","year":"2007","unstructured":"Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3), 1\u201339 (2007)","journal-title":"Physiol. Meas."},{"issue":"8","key":"6_CR4","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1007\/s10439-005-5763-2","volume":"33","author":"FP Wieringa","year":"2005","unstructured":"Wieringa, F.P., Mastik, F., Steen, A.: Contactless multiple wavelength photoplethysmographic imaging: a first step toward \u201cspo2 camera\u201d technology. Ann. Biomed. Eng. 33(8), 1034\u20131041 (2005)","journal-title":"Ann. Biomed. Eng."},{"issue":"8","key":"6_CR5","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1016\/j.medengphy.2006.09.006","volume":"29","author":"C Takano","year":"2007","unstructured":"Takano, C., Ohta, Y.: Heart rate measurement based on a time-lapse image. Med. Eng. Phys. 29(8), 853\u2013857 (2007)","journal-title":"Med. Eng. Phys."},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Hu, S., Zheng, J., Chouliaras, V., Summers, R.: Feasibility of imaging photoplethysmography. In: International Conference on Biomedical Engineering & Informatics pp. 72-75 (2008)","DOI":"10.1109\/BMEI.2008.365"},{"issue":"10","key":"6_CR7","doi-asserted-by":"publisher","first-page":"10762","DOI":"10.1364\/OE.18.010762","volume":"18","author":"MZ Poh","year":"2010","unstructured":"Poh, M.Z., Mcduff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18(10), 10762\u201310774 (2010)","journal-title":"Opt. Express"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Rubins, U., Upmalis, V., Rubenis, O., Jakovels, D., Spigulis, J.: Real-time photoplethysmography imaging system. In: 15th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, pp. 183\u2013186 (2011)","DOI":"10.1007\/978-3-642-21683-1_46"},{"issue":"6","key":"6_CR9","doi-asserted-by":"publisher","first-page":"61205","DOI":"10.1117\/1.JBO.18.6.061205","volume":"18","author":"Y Sun","year":"2013","unstructured":"Sun, Y., Hu, S., Azorin-Peris, V., Kalawsky, R., Greenwald, S.: Noncontact imaging photo- plethysmography to effectively access pulse rate variability. J. Biomed. Opt. 18(6), 61205 (2013)","journal-title":"J. Biomed. Opt."},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Sheth, R., Lu, W., Yu, Y., Fedkiw, R.P.: Fully momentum-conserving reduced deformable bodies with collision, contact, articulation, and skinning. In: the 14th ACM SIGGRAPH\/ES, pp. 45\u201354 (2015)","DOI":"10.1145\/2786784.2786787"},{"issue":"2","key":"6_CR11","first-page":"482","volume":"62","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Toyoda, K.: Ohtsuki: Blind source separation on non-contact heartbeat detection by non-negative matrix factorization algorithms. IEEE Trans. Biomed. Eng. 62(2), 482\u2013494 (2020)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.optcom.2014.12.032","volume":"341","author":"G Cui","year":"2015","unstructured":"Cui, G., Feng, H., Xu, Z., Chen, Y.: Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Optics Communications 341, 199\u2013209 (2015)","journal-title":"Optics Communications"},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.infrared.2015.10.004","volume":"73","author":"B Zhang","year":"2015","unstructured":"Zhang, B., Lu, X., Pei, H., Ying, Z.: A fusion algorithm for infrared and visible images based on saliency analysis and non-subsampled Shearlet transform. Infrared Phys. Technol. 73, 286\u2013297 (2015)","journal-title":"Infrared Phys. Technol."},{"key":"6_CR14","unstructured":"Hui, L., Wu, X.J., Kittler, J.: Infrared and visible image fusion using a deep learning framework. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2705\u2013 2710 (2018)"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Prabhakar, K.R., Srikar, V.S., Babu, R.V.: DeepFuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs. In: ICCV, pp. 4724\u20134732 (2017)","DOI":"10.1109\/ICCV.2017.505"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Xu, H., Ma, J., Le, Z., Guo, X.: FusionDN: a unified densely connected network for image fusion. In: Proceedings of the AAAI Conference on Artificial Intelligence 34, pp. 12484\u201312491 (2020)","DOI":"10.1609\/aaai.v34i07.6936"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Laurens, V., Weinberger, K.: Densely connected convolutional networks. In: CVPR, pp. 2261\u20132269 (2016)","DOI":"10.1109\/CVPR.2017.243"},{"key":"6_CR18","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","volume":"54","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Liu, Y., Sun, P., Yan, H., Zhao, X., Zhang, L.: IFCNN: A general image fusion framework based on convolutional neural network. Inf. Fusion 54, 99\u2013118 (2020)","journal-title":"Inf. Fusion"},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TCI.2020.2965304","volume":"6","author":"R Hou","year":"2020","unstructured":"Hou, R., et al.: VIF-Net: an unsupervised framework for infrared and visible image fusion. IEEE Trans. Comput. Imaging 6, 640\u2013651 (2020)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Fu, Y., Wu, X.J.: A Dual-branch network for infrared and visible image fusion. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 10675\u201310680 (2020)","DOI":"10.1109\/ICPR48806.2021.9412293"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Zou, J., Zhang, S., Ge, B.: On the target area tracking method for heart rate measurement using deep learning strategy. In: 2019 11th International Conference on Digital Image Processing, pp. 469\u2013476 (2019)","DOI":"10.1117\/12.2539746"},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1109\/TBME.2016.2609282","volume":"64","author":"W Wang","year":"2017","unstructured":"Wang, W., Brinker, A.C., Stuijk, S., Haan, G.: Algorithmic principles of remote PPG. IEEE Trans. Biomed Eng. 64, 1479\u20131491 (2017)","journal-title":"IEEE Trans. Biomed Eng."},{"issue":"1","key":"6_CR23","first-page":"87","volume":"47","author":"R Qin","year":"2021","unstructured":"Qin, R., Chen, Z.: Non-contact stable heart rate measurement algorithm under face motion conditions. Optical Technol. 47(1), 87\u201392 (2021)","journal-title":"Optical Technol."},{"key":"6_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"issue":"3","key":"6_CR25","doi-asserted-by":"publisher","first-page":"811","DOI":"10.32604\/iasc.2021.016516","volume":"28","author":"Z Li","year":"2021","unstructured":"Li, Z., Zou, J., Yan, P., Hong, D.: Non-contact real-time monitoring of driver\u2019s physiological parameters under ambient light condition. Intell. Autom. Soft Comput. 28(3), 811\u2013822 (2021)","journal-title":"Intell. Autom. Soft Comput."},{"issue":"1","key":"6_CR26","doi-asserted-by":"publisher","first-page":"213","DOI":"10.32604\/iasc.2021.016201","volume":"28","author":"P Yan","year":"2021","unstructured":"Yan, P., Zou, J., Li, Z., Yang, X.: Infrared and visible image fusion based on nsst and rdn. Intell. Autom. Soft Comput. 28(1), 213\u2013225 (2021)","journal-title":"Intell. Autom. Soft Comput."},{"issue":"1","key":"6_CR27","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.32604\/cmc.2022.022679","volume":"71","author":"C Yen","year":"2022","unstructured":"Yen, C., Liao, C.: Blood pressure and heart rate measurements using photoplethysmography with modified lrcn. Comput., Mater. Continua 71(1), 1973\u20131986 (2022)","journal-title":"Comput., Mater. Continua"},{"issue":"1","key":"6_CR28","doi-asserted-by":"publisher","first-page":"377","DOI":"10.32604\/iasc.2022.017748","volume":"32","author":"W Zeng","year":"2022","unstructured":"Zeng, W., Sheng, Y., Hu, Q., Huo, Z., Zhang, Y.: Heart rate detection using svm based on video imagery. Intell. Autom. Soft Comput. 32(1), 377\u2013387 (2022)","journal-title":"Intell. Autom. Soft Comput."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06788-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T12:20:24Z","timestamp":1657714824000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06788-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031067877","9783031067884"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06788-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qinghai","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 July 2022","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":"incodldos2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icaisconf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1124","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":"115","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":"53","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":"10% - 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":"8","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)"}}]}}