{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T12:23:19Z","timestamp":1759580599622,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031376597"},{"type":"electronic","value":"9783031376603"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-37660-3_39","type":"book-chapter","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:02:20Z","timestamp":1690610540000},"page":"558-573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Learning for\u00a0Remote Heart Rate Estimation: A Reproducible and\u00a0Optimal State-of-the-Art Framework"],"prefix":"10.1007","author":[{"given":"Nelida","family":"Mirabet-Herranz","sequence":"first","affiliation":[]},{"given":"Khawla","family":"Mallat","sequence":"additional","affiliation":[]},{"given":"Jean-Luc","family":"Dugelay","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"39_CR1","doi-asserted-by":"crossref","unstructured":"Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Measur. 28(3) (2007)","DOI":"10.1088\/0967-3334\/28\/3\/R01"},{"key":"39_CR2","doi-asserted-by":"crossref","unstructured":"Blazek, V.: Ambient and unobtrusive cardiorespiratory monitoring. In: 2016 ELEKTRO. IEEE (2016)","DOI":"10.1109\/ELEKTRO.2016.7512022"},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Bousefsaf, F., Pruski, A., Maaoui, C.: 3d convolutional neural networks for remote pulse rate measurement and mapping from facial video. Appl. Sci. 9(20) (2019)","DOI":"10.3390\/app9204364"},{"key":"39_CR4","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":"Weixuan Chen","year":"2018","unstructured":"Chen, Weixuan, McDuff, Daniel: DeepPhys: video-based physiological measurement using convolutional attention networks. In: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, Weiss, Yair (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":"39_CR5","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."},{"key":"39_CR6","doi-asserted-by":"crossref","unstructured":"Hernandez-Ortega, J., Fierrez, J., Morales, A., Diaz, D.: A comparative evaluation of heart rate estimation methods using face videos. In: 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE (2020)","DOI":"10.1109\/COMPSAC48688.2020.00-53"},{"key":"39_CR7","unstructured":"Heusch, G., Anjos, A., Marcel, S.: A reproducible study on remote heart rate measurement. arXiv preprint arXiv:1709.00962 (2017)"},{"key":"39_CR8","doi-asserted-by":"crossref","unstructured":"Hu, M., Qian, F., Wang, X., He, L., Guo, D., Ren, F.: Robust heart rate estimation with spatial-temporal attention network from facial videos. IEEE Trans. Cognit. Dev. Syst. (2021)","DOI":"10.1109\/TCDS.2021.3062370"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Huang, B., Chang, C.M., Lin, C.L., Chen, W., Juang, C.F., Wu, X.: Visual heart rate estimation from facial video based on CNN. In: 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE (2020)","DOI":"10.1109\/ICIEA48937.2020.9248356"},{"key":"39_CR10","unstructured":"Kwon, S., Kim, J., Lee, D., Park, K.: Roi analysis for remote photoplethysmography on facial video. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2015)"},{"issue":"7","key":"39_CR11","first-page":"1275","volume":"23","author":"PS Lamba","year":"2020","unstructured":"Lamba, P.S., Virmani, D.: Contactless heart rate estimation from face videos. J. Stat. Manage. Syst. 23(7), 1275\u20131284 (2020)","journal-title":"J. Stat. Manage. Syst."},{"key":"39_CR12","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":"39_CR13","doi-asserted-by":"crossref","unstructured":"Lokendra, B., Puneet, G.: And-RPPG: a novel denoising-RPPG network for improving remote heart rate estimation. Comput. Biol. Med. 105146 (2021)","DOI":"10.1016\/j.compbiomed.2021.105146"},{"key":"39_CR14","doi-asserted-by":"crossref","unstructured":"Mehta, A.D., Sharma, H.: Heart rate estimation from RGB facial videos using robust face demarcation and VMD. In: 2021 National Conference on Communications (NCC), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/NCC52529.2021.9530067"},{"issue":"4","key":"39_CR15","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/JBHI.2013.2291900","volume":"18","author":"H Monkaresi","year":"2013","unstructured":"Monkaresi, H., Calvo, R.A., Yan, H.: A machine learning approach to improve contactless heart rate monitoring using a webcam. IEEE J. Biomed. Health Inform. 18(4), 1153\u20131160 (2013)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Niu, X., Han, H., Shan, S., Chen, X.: Continuous heart rate measurement from face: A robust rppg approach with distribution learning. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 642\u2013650. IEEE (2017)","DOI":"10.1109\/BTAS.2017.8272752"},{"key":"39_CR17","doi-asserted-by":"crossref","unstructured":"Niu, X., Han, H., Shan, S., Chen, X.: Synrhythm: learning a deep heart rate estimator from general to specific. In: 2018 24th International Conference on Pattern Recognition (ICPR). IEEE (2018)","DOI":"10.1109\/ICPR.2018.8546321"},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Niu, X., Shan, S., Han, H., Chen, X.: Rhythmnet: end-to-end heart rate estimation from face via spatial-temporal representation. IEEE Trans. Image Process. 29 (2019)","DOI":"10.1109\/TIP.2019.2947204"},{"key":"39_CR19","doi-asserted-by":"crossref","unstructured":"Niu, X., et al.: Robust remote heart rate estimation from face utilizing spatial-temporal attention. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) (2019)","DOI":"10.1109\/FG.2019.8756554"},{"key":"39_CR20","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":"39_CR21","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. Biome. Eng. 58(1), 7\u201311 (2010)","journal-title":"IEEE Trans. Biome. Eng."},{"issue":"10","key":"39_CR22","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. Optics Express 18(10), 10762\u201310774 (2010)","journal-title":"Optics Express"},{"key":"39_CR23","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Liu, Y., Arteaga-Falconi, J., Dong, H., El Saddik, A.: EVM-CNN: real-time contactless heart rate estimation from facial video. IEEE Trans. Multimedia 21(7) (2018)","DOI":"10.1109\/TMM.2018.2883866"},{"key":"39_CR24","unstructured":"Rahman, H., Ahmed, M.U., Begum, S., Funk, P.: Real time heart rate monitoring from facial rgb color video using webcam. In: The 29th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS), 2\u20133 June 2016, Malm\u00f6, Sweden. No. 129, Link\u00f6ping University Electronic Press (2016)"},{"key":"39_CR25","doi-asserted-by":"crossref","unstructured":"Siddiqui, H., Rattani, A., Kisku, D.R., Dean, T.: AI-based BMI inference from facial images: an application to weight monitoring. preprint arXiv:2010.07442 (2020)","DOI":"10.1109\/ICMLA51294.2020.00177"},{"key":"39_CR26","doi-asserted-by":"crossref","unstructured":"Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1) (2012)","DOI":"10.1109\/T-AFFC.2011.25"},{"issue":"12","key":"39_CR27","doi-asserted-by":"publisher","first-page":"13484","DOI":"10.1109\/JSEN.2021.3067770","volume":"21","author":"R Song","year":"2021","unstructured":"Song, R., Li, J., Wang, M., Cheng, J., Li, C., Chen, X.: Remote photoplethysmography with an EEMD-MCCA method robust against spatially uneven illuminations. IEEE Sens. J. 21(12), 13484\u201313494 (2021)","journal-title":"IEEE Sens. J."},{"key":"39_CR28","doi-asserted-by":"crossref","unstructured":"Song, R., Zhang, S., Li, C., Zhang, Y., Cheng, J., Chen, X.: Heart rate estimation from facial videos using a spatiotemporal representation with convolutional neural networks. IEEE Trans. Instrument. Measur. 69(10) (2020)","DOI":"10.1109\/TIM.2020.2984168"},{"key":"39_CR29","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 (2018)"},{"key":"39_CR30","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J.F., Sebe, N.: Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2396\u20132404 (2016)","DOI":"10.1109\/CVPR.2016.263"},{"key":"39_CR31","doi-asserted-by":"crossref","unstructured":"Van Kampen, E., Zijlstra, W.G.: Determination of hemoglobin and its derivatives. Adv. Clin. Chem. 8 (1966)","DOI":"10.1016\/S0065-2423(08)60414-X"},{"key":"39_CR32","doi-asserted-by":"crossref","unstructured":"Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Optics Express 16(26) (2008)","DOI":"10.1364\/OE.16.021434"},{"key":"39_CR33","doi-asserted-by":"crossref","unstructured":"Wang, G.: Influence of roi selection for remote photoplethysmography with singular spectrum analysis. In: 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID), pp. 416\u2013420. IEEE (2021)","DOI":"10.1109\/AIID51893.2021.9456548"},{"key":"39_CR34","doi-asserted-by":"crossref","unstructured":"Wang, W., den Brinker, A.C., Stuijk, S., De Haan, G.: Algorithmic principles of remote PPG. IEEE Trans. Biomed. Eng. 64(7) (2016)","DOI":"10.1109\/TBME.2016.2609282"},{"key":"39_CR35","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, W., Song, J.: Learning spatiotemporal features with 3DCNN and convgru for video anomaly detection. In: 2018 14th IEEE International Conference on Signal Processing (ICSP), pp. 474\u2013479. IEEE (2018)","DOI":"10.1109\/ICSP.2018.8652354"},{"key":"39_CR36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Dantcheva, A.: A video is worth more than 1000 lies. comparing 3DCNN approaches for detecting deepfakes. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 515\u2013519. IEEE (2020)","DOI":"10.1109\/FG47880.2020.00089"},{"key":"39_CR37","doi-asserted-by":"crossref","unstructured":"Wang, Z.K., Kao, Y., Hsu, C.T.: Vision-based heart rate estimation via a two-stream CNN. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 3327\u20133331. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803649"},{"key":"39_CR38","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, X., Cheng, K.T.: Accurate face alignment and adaptive patch selection for heart rate estimation from videos under realistic scenarios. PLoS ONE 13(5), e0197275 (2018)","DOI":"10.1371\/journal.pone.0197275"},{"key":"39_CR39","unstructured":"Yu, Z., Li, X., Zhao, G.: Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks. arXiv preprint arXiv:1905.02419 (2019)"},{"key":"39_CR40","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 International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00024"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37660-3_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:08:48Z","timestamp":1690610928000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37660-3_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031376597","9783031376603"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37660-3_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montr\u00e9al, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"21 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icpr2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}