{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:31:18Z","timestamp":1743150678321,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031189098"},{"type":"electronic","value":"9783031189104"}],"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\/978-3-031-18910-4_37","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"454-466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatial-Channel Mixed Attention Based Network for\u00a0Remote Heart Rate Estimation"],"prefix":"10.1007","author":[{"given":"Bixiao","family":"Ling","sequence":"first","affiliation":[]},{"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jianjun","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Durand, F., Guttag, J.: Detecting pulse from head motions in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3430\u20133437 (2013)","DOI":"10.1109\/CVPR.2013.440"},{"key":"37_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/978-3-030-01216-8_22","volume-title":"Computer Vision \u2013 ECCV 2018","author":"W Chen","year":"2018","unstructured":"Chen, W., McDuff, D.: DeepPhys: video-based physiological measurement using convolutional attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 356\u2013373. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_22"},{"issue":"10","key":"37_CR3","doi-asserted-by":"publisher","first-page":"2878","DOI":"10.1109\/TBME.2013.2266196","volume":"60","author":"G De Haan","year":"2013","unstructured":"De Haan, G., Jeanne, V.: Robust pulse rate from chrominance-based RPPG. IEEE Trans. Biomed. Eng. 60(10), 2878\u20132886 (2013)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"9","key":"37_CR4","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1088\/0967-3334\/35\/9\/1913","volume":"35","author":"G De Haan","year":"2014","unstructured":"De Haan, G., Van Leest, A.: Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiol. Meas. 35(9), 1913 (2014)","journal-title":"Physiol. Meas."},{"key":"37_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"8","key":"37_CR6","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1109\/TBME.2007.891930","volume":"54","author":"M Garbey","year":"2007","unstructured":"Garbey, M., Sun, N., Merla, A., Pavlidis, I.: Contact-free measurement of cardiac pulse based on the analysis of thermal imagery. IEEE Trans. Biomed. Eng. 54(8), 1418\u20131426 (2007)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"37_CR9","unstructured":"Kwon, S., Kim, J., Lee, D., Park, K.: Roi analysis for remote photoplethysmography on facial video. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4938\u20134941. IEEE (2015)"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Lam, A., Kuno, Y.: Robust heart rate measurement from video using select random patches. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3640\u20133648 (2015)","DOI":"10.1109\/ICCV.2015.415"},{"key":"37_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/978-3-030-58583-9_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"E Lee","year":"2020","unstructured":"Lee, E., Chen, E., Lee, C.-Y.: Meta-rPPG: remote heart rate estimation using a transductive meta-learner. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 392\u2013409. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58583-9_24"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Lempe, G., Zaunseder, S., Wirthgen, T., Zipser, S., Malberg, H.: Roi selection for remote photoplethysmography. In: Bildverarbeitung f\u00fcr die Medizin 2013, pp. 99\u2013103. Springer, Heidelberg (2013)","DOI":"10.1007\/978-3-642-36480-8_19"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, J., Zhao, G., Pietikainen, M.: Remote heart rate measurement from face videos under realistic situations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4264\u20134271 (2014)","DOI":"10.1109\/CVPR.2014.543"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Lu, H., Han, H., Zhou, S.K.: Dual-gan: Joint bvp and noise modeling for remote physiological measurement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12404\u201312413 (2021)","DOI":"10.1109\/CVPR46437.2021.01222"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"McDuff, D.: Deep super resolution for recovering physiological information from videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1367\u20131374 (2018)","DOI":"10.1109\/CVPRW.2018.00185"},{"issue":"10","key":"37_CR16","doi-asserted-by":"publisher","first-page":"2593","DOI":"10.1109\/TBME.2014.2323695","volume":"61","author":"D McDuff","year":"2014","unstructured":"McDuff, D., Gontarek, S., Picard, R.W.: Improvements in remote cardiopulmonary measurement using a five band digital camera. IEEE Trans. Biomed. Eng. 61(10), 2593\u20132601 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"37_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-3-030-20873-8_36","volume-title":"Computer Vision \u2013 ACCV 2018","author":"X Niu","year":"2019","unstructured":"Niu, X., Han, H., Shan, S., Chen, X.: VIPL-HR: a multi-modal database for pulse estimation from less-constrained face video. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11365, pp. 562\u2013576. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20873-8_36"},{"key":"37_CR18","first-page":"2409","volume":"29","author":"X Niu","year":"2019","unstructured":"Niu, X., Shan, S., Han, H., Chen, X.: Rhythmnet: end-to-end heart rate estimation from face via spatial-temporal representation. TIP 29, 2409\u20132423 (2019)","journal-title":"TIP"},{"key":"37_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/978-3-030-58536-5_18","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Niu","year":"2020","unstructured":"Niu, X., Yu, Z., Han, H., Li, X., Shan, S., Zhao, G.: Video-based remote physiological measurement via cross-verified feature disentangling. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 295\u2013310. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_18"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Niu, X., Zhao, X., Han, H., Das, A., Dantcheva, A., Shan, S., Chen, X.: Robust remote heart rate estimation from face utilizing spatial-temporal attention. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/FG.2019.8756554"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Perepelkina, O., Artemyev, M., Churikova, M., Grinenko, M.: Hearttrack: Convolutional neural network for remote video-based heart rate monitoring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 288\u2013289 (2020)","DOI":"10.1109\/CVPRW50498.2020.00152"},{"issue":"1","key":"37_CR22","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/TBME.2010.2086456","volume":"58","author":"MZ Poh","year":"2010","unstructured":"Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7\u201311 (2010)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"10","key":"37_CR23","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":"37_CR24","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Pourreza, M., Li, X., Fathy, M., Zhao, G.: Deep-hr: Fast heart rate estimation from face video under realistic conditions. arXiv preprint arXiv:2002.04821 (2020)","DOI":"10.1016\/j.eswa.2021.115596"},{"key":"37_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"issue":"1","key":"37_CR26","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","volume":"3","author":"M Soleymani","year":"2011","unstructured":"Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1), 42\u201355 (2011)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"37_CR27","unstructured":"\u0160petl\u00edk, R., Franc, V., Matas, J.: Visual heart rate estimation with convolutional neural network. In: Proceedings of the british machine vision conference, Newcastle, UK, pp. 3\u20136 (2018)"},{"key":"37_CR28","doi-asserted-by":"crossref","unstructured":"Stricker, R., M\u00fcller, S., Gross, H.M.: Non-contact video-based pulse rate measurement on a mobile service robot. In: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 1056\u20131062. IEEE (2014)","DOI":"10.1109\/ROMAN.2014.6926392"},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Ulyanov, S.S., Tuchin, V.V.: Pulse-wave monitoring by means of focused laser beams scattered by skin surface and membranes. In: Static and Dynamic Light Scattering in Medicine and Biology, vol. 1884, pp. 160\u2013167. International Society for Optics and Photonics (1993)","DOI":"10.1117\/12.148363"},{"issue":"26","key":"37_CR30","doi-asserted-by":"publisher","first-page":"21434","DOI":"10.1364\/OE.16.021434","volume":"16","author":"W Verkruysse","year":"2008","unstructured":"Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16(26), 21434\u201321445 (2008)","journal-title":"Opt. Express"},{"issue":"7","key":"37_CR31","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1109\/TBME.2016.2609282","volume":"64","author":"W Wang","year":"2016","unstructured":"Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Algorithmic principles of remote ppg. IEEE Trans. Biomed. Eng. 64(7), 1479\u20131491 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"37_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"37_CR33","doi-asserted-by":"crossref","unstructured":"Yu, Z., Peng, W., Li, X., Hong, X., Zhao, G.: Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 151\u2013160 (2019)","DOI":"10.1109\/ICCV.2019.00024"}],"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\/978-3-031-18910-4_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:35:32Z","timestamp":1666827332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18910-4_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189098","9783031189104"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18910-4_37","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":"27 October 2022","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":"Shenzhen","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":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","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":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","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":"233","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":"41% - 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":"3.03","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":"3.35","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}