研究介紹: 電腦視覺技術的最新進展有助於提高遠端光電體積描記法 (rPPG) 的準確 性。即使存在運動偽影和光線變化，rPPG 管道中也使用臉部對齊來提取臉部像素。 然而，需要研究人臉對齊對 rPPG 準確度和精確度的影響。
研究目標: 我們的目的是研究臉部對齊在 rPPG 中的作用，探索它是否可以提高心率 (HR) 估計的準確性和精確度。
研究方法: 我們評估了兩種人臉對齊方法的影響——3D 位置回歸網路 (3D PRN) 和密 集人臉對齊版本 2 (3DDFAV2)，在基於色度的 rPPG (CHROM) 管道中進行 HR 估計。 我們使用實驗室收集的 18 名受試者在 5 個不同場景中的臉部影片和心電圖資料。在 這些場景中，物件要麼坐著不動(靜態)、說話、騎自行車或旋轉頭部。我們也進行 了五項靈敏度測試——幀交換、色度值無效、間隔移位、像素遺失和間隔縮小。這 是透過操縱 30 秒的時間間隔並執行多次蒙特卡羅模擬來找出數據中的微小變化如何 影響輸出 HR 估計來完成的。
研究結果: 我們計算成功 HR 估計的平均絕對誤差 (MAE)，絕對誤差小於 10 bpm。 CHROM 方法在口語測試(其中 CHROM-PRN 具有最低的 MAE)之外的所有場景 中均表現出最低的 MAE。在所有方法中，對於高達 40% 的幀交換、高達 70% 的色 度值無效以及高達 90% 的皮膚像素丟棄，HR 相對於原始估計的平均差異保持在 1 bpm 以下。我們最初使用 30 秒的間隔。然而，可以將間隔縮短至 10-15 秒，低於該時間，HR 的平均差異將超過 1 bpm。
研究結論: 根據我們的研究結果，我們得出結論，在 rPPG 管道中使用 3D 人臉對齊 方法不會顯著影響 HR 估計值。儘管還需要進一步驗證，但這些發現對於簡化 rPPG 系統並提高其準確性具有重要意義。

Introduction: Recent advances in computer vision technology have helped improve the accuracy of remote photoplethysmography (rPPG). Face alignment is used in the pipeline of rPPG to extract the pixels of the face even when there are motion artifacts and light changes. However, there is a need to study the impact of face alignment on the accuracy and precision of rPPG.
Objectives: We aim to investigate the role of face alignment in rPPG, exploring whether it enhances the accuracy and precision of heart rate (HR) estimation.
Methods: We evaluate the impact of two face alignment methods – 3D Position Regression Network (3D PRN) and Dense Face alignment Version 2 (3DDFAV2), in the Chrominance- based rPPG (CHROM) pipeline for HR estimation. We use face videos and ECG data from 18 subjects in five different scenarios, collected in our lab. In these scenarios, the subject is either sitting still (static), speaking, biking, or rotating the head. We also perform five sensi- tivity tests – frame swapping, chrominance value nullification, interval shift, pixel dropping and interval shrinking. This is done by manipulating a 30-second interval and performing multiple Monte Carlo simulations to find out how small changes in the data affect the output HR estimation.
Results: We calculate the mean absolute error (MAE) of the successful HR estimates with an absolute error of less than 10 bpm. The CHROM method demonstrates the lowest MAE in all scenarios except for the speaking test, in which CHROM-PRN has the lowest MAE. Across all methods, the mean difference in HR relative to the original estimate remains below 1 bpm for up to 40% of frame swapping, up to 70% of chrominance value nullification, and up to 90% of skin pixel dropping. We initially used 30 seconds interval. However, shrinking the interval is possible up to 10-15 seconds, below which the mean difference in HR becomes more than 1 bpm.
Conclusion: Based on our findings, we conclude that using 3D face alignment methods in the rPPG pipeline does not significantly impact HR estimation. These findings have implications for simplifying rPPG systems and improving their accuracy, although further validation is needed.

Asthana, A., Zafeiriou, S., Cheng, S., and Pantic, M. (2013). Robust discriminative response map fitting with constrained local models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3444–3451. IEEE.
Balakrishnan, G., Durand, F., and Guttag, J. (2013a). Detecting Pulse from Head Motions in Video. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 3430–3437. IEEE.

Balakrishnan, G., Durand, F., and Guttag, J. (2013b). Detecting pulse from head motions in video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3430–3437.

Basilio, J. A. M., Torres, G. A., Pérez, G. S., Medina, L. K. T., and Meana, H. M. P. (2011). Explicit image detection using ycbcr space color model as skin detection. Applications of Mathematics and Computer Engineering, pages 123–128.

Blackford, E. B. and Estepp, J. R. (2015). Effects of frame rate and image resolution on pulse rate measured using multiple camera imaging photoplethysmography. In Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 9417, page 94172D. International Society for Optics and Photonics.

Blackford, E. B., Estepp, J. R., Piasecki, A. M., Bowers, M. A., and Klosterman, S. L. (2016). Long-range non-contact imaging photoplethysmography: cardiac pulse wave sensing at a distance. In Optical Diagnostics and Sensing XVI: Toward Point-of-Care Diagnostics, volume 9715, page 971512. International Society for Optics and Photonics.

Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., and Dubois, J. (2019). Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recognition Letters, 124:82–90.

Bonmassar, G., Purdon, P. L., Jääskeläinen, I. P., Chiappa, K., Solo, V., Brown, E. N., and Belliveau, J. W. (2002). Motion and ballistocardiogram artifact removal for interleaved recording of eeg and eps during mri. Neuroimage, 16(4):1127–1141.

Botina-Monsalve, D., Benezeth, Y., and Miteran, J. (2022). Rtrppg: An ultra light 3dcnn for real-time remote photoplethysmography. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2146–2154.

Bouguet, J.-Y. et al. (2001). Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel corporation, 5(1-10):4.

Bradski, G. (2000a). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.

Bradski, G. (2000b). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.

Bulat, A. and Tzimiropoulos, G. (2017). How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks). 2017 IEEE International Conference on Computer Vision (ICCV).

Burgos-Artizzu, X. P., Perona, P., and Dollár, P. (2013). Robust face landmark estimation under occlusion. In Proceedings of the IEEE international conference on computer vision, pages 1513–1520.

Cao, X., Wei, Y., Wen, F., and Sun, J. (2014). Face alignment by explicit shape regression. International journal of computer vision, 107:177–190.

Chang, C.-M., Hung, C.-C., Zhao, C., Lin, C.-L., and Hsu, B.-Y. (2020). Learning-based remote photoplethysmography for physiological signal feedback control in fitness training. In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), pages 1663–1668. IEEE.

Chang, H.-C. (2023). Noncontact lie detection system involving photoplethysmography and heart rate variability. International Journal of Pattern Recognition and Artificial Intelligence, page 2350006.

Cheang, P. Y. and Smith, P. R. (2003). An overview of non-contact photoplethysmography. Electronic systems and control division research, pages 57–59.

Chen, S., Ho, S. K., Chin, J. W., Luo, K. H., Chan, T. T., So, R. H., and Wong, K. L. (2023). Deep learning-based image enhancement for robust remote photoplethysmography in various illumination scenarios. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6076–6084.

Chen, W. and McDuff, D. (2018). Deepphys: Video-based physiological measurement using convolutional attention networks. In Proceedings of the European Conference on Computer Vision (ECCV), pages 349–365.

Da He, D., Winokur, E. S., and Sodini, C. G. (2011). A continuous, wearable, and wireless heart monitor using head ballistocardiogram (bcg) and head electrocardiogram (ecg). In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 4729–4732. IEEE.

Datcu, D., Cidota, M., Lukosch, S., and Rothkrantz, L. (2013). Noncontact automatic heart rate analysis in visible spectrum by specific face regions. In Proceedings of the 14th International Conference on Computer Systems and Technologies, pages 120–127.

De Haan, G. and Jeanne, V. (2013). Robust pulse rate from chrominance-based rppg. IEEE Transactions on Biomedical Engineering, 60(10):2878–2886.

De Haan, G. and Van Leest, A. (2014). Improved motion robustness of remote-ppg by using the blood volume pulse signature. Physiological measurement, 35(9):1913.
De la Torre Frade, F., Chu, W.-S., Xiong, X., Carrasco, F. V., Ding, X., and Cohn, J. (2015).

Intraface. In Automatic face and gesture recognition.

de Wijk, R. A., Ushiama, S., Ummels, M., Zimmerman, P., Kaneko, D., and Vingerhoeds, M. H. (2021). Reading food experiences from the face: effects of familiarity and branding of soy sauce on facial expressions and video-based rppg heart rate. Foods, 10(6):1345.

FaceHeart (2023a). Faceheart has obtained fda 510(k) clearance for its video-based contact-less vital sign measurement.

https://www.faceheart.com/news_detail.php?id= 22#:~:text=FaceHeart%2C%20an%20AI%2Dbased%20provider,sign%20solution% 2C%20FaceHeart%20Vitals%E2%84%A2. Last accessed: 2023-11-22.
FaceHeart (2023b). Faceheart vitals software development kit (fh vitals sdk) fda approval. https://www.accessdata.fda.gov/cdrh_docs/pdf22/K223622.pdf. Last accessed: 2023-11-22.

Fan, X. and Wang, J. (2015). Bayesheart: A probabilistic approach for robust, low-latency heart rate monitoring on camera phones. In Proceedings of the 20th International Conference on Intelligent User Interfaces, pages 405–416.

Feng, Y., Wu, F., Shao, X., Wang, Y., and Zhou, X. (2018a). Joint 3d face reconstruction and dense alignment with position map regression network. In Proceedings of the European conference on computer vision (ECCV), pages 534–551.

Feng, Y., Wu, F., Shao, X., Wang, Y., and Zhou, X. (2018b). Joint 3d face reconstruction and dense alignment with position map regression network. In Proceedings of the European conference on computer vision (ECCV), pages 534–551.

Fouad, R., Omer, O. A., and Aly, M. H. (2019). Optimizing remote photoplethysmogra- phy using adaptive skin segmentation for real-time heart rate monitoring. IEEE Access, 7:76513–76528.

Guo, J., Zhu, X., Yang, Y., Yang, F., Lei, Z., and Li, S. Z. (2020). Towards fast, accurate and stable 3d dense face alignment. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIX, pages 152–168. Springer.

Haque, M. A., Irani, R., Nasrollahi, K., and Moeslund, T. B. (2016). Heartbeat Rate Measurement from Facial Video. IEEE Intelligent Systems, 31(3):40–48.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.

Hertzman, A. B. (1937). Photoelectric plethysmography of the fingers and toes in man. Proceedings of the Society for Experimental Biology and Medicine, 37(3):529–534.

Hertzman, A. B. (1938). The blood supply of various skin areas as estimated by the photoelectric plethysmograph. American Journal of Physiology–Legacy Content, 124(2):328–340.

Hilmisson, H., Berman, S., and Magnusdottir, S. (2020). Sleep apnea diagnosis in children using software-generated apnea-hypopnea index (ahi) derived from data recorded with a single photoplethysmogram sensor (ppg) results from the childhood adenotonsillectomy study (chat) based on cardiopulmonary coupling analysis. Sleep and Breathing, 24:1739– 1749.

Hsu, G.-S., Ambikapathi, A., and Chen, M.-S. (2017). Deep learning with time-frequency representation for pulse estimation from facial videos. In 2017 IEEE International Joint Conference on Biometrics (IJCB), pages 383–389. IEEE.

Hsu, Y., Lin, Y.-L., and Hsu, W. (2014). Learning-based heart rate detection from remote photoplethysmography features. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4433–4437. IEEE.

Hu, M., Qian, F., Guo, D., Wang, X., He, L., and Ren, F. (2021). Eta-rppgnet: Effective time-domain attention network for remote heart rate measurement. IEEE Transactions on Instrumentation and Measurement, 70:1–12.

Huang, P.-W., Wu, B.-J., and Wu, B.-F. (2020). A heart rate monitoring framework for real-world drivers using remote photoplethysmography. IEEE journal of biomedical and health informatics.

Hülsbusch, M. and Rembold, B. (2008). Ein bildgestütztes, funktionelles verfahren zur op- toelektronischen erfassung der hautperfusion. Technical report, Lehrstuhl und Institut für Hochfrequenztechnik.

Irani, R., Nasrollahi, K., and Moeslund, T. B. (2014). Improved pulse detection from head motions using dct. In 2014 international conference on computer vision theory and applications (VISAPP), volume 3, pages 118–124. IEEE.

Jansen, B. H., Larson, B. H., and Shankar, K. (1991). Monitoring of the ballistocardio- gram with the static charge sensitive bed. IEEE Transactions on Biomedical Engineering, 38(8):748–751.

Karlen, W., Lim, J., Ansermino, J. M., Dumont, G., and Scheffer, C. (2012). Design challenges for camera oximetry on a mobile phone. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 2448–2451. IEEE.

Kazemi, V. and Sullivan, J. (2014a). One millisecond face alignment with an ensemble of regression trees. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 1867–1874.

Kazemi, V. and Sullivan, J. (2014b). One millisecond face alignment with an ensemble of regression trees. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 1867–1874.

King, D. E. (2009). Dlib-ml: A machine learning toolkit. The Journal of Machine Learning Research, 10:1755–1758.

Koivistoinen, T., Junnila, S., Varri, A., and Koobi, T. (2004). A new method for measuring the ballistocardiogram using emfi sensors in a normal chair. In The 26th annual international conference of the IEEE engineering in medicine and biology society, volume 1, pages 2026–2029. IEEE.

Kowalski, M., Naruniec, J., and Trzcinski, T. (2017a). Deep alignment network: A convolutional neural network for robust face alignment.

Kowalski, M., Naruniec, J., and Trzcinski, T. (2017b). Deep alignment network: A convolutional neural network for robust face alignment. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 88–97.

Kuncoro, C. B. D., Luo, W.-J., and Kuan, Y.-D. (2020). Wireless photoplethysmography sensor for continuous blood pressure biosignal shape acquisition. Journal of Sensors, 2020:1– 9.

Kwon, S., Kim, H., and Park, K. S. (2012). Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. In 2012 annual international conference of the IEEE engineering in medicine and biology society, pages 2174–2177. IEEE.

Lee, E., Chen, E., and Lee, C.-Y. (2020). Meta-rppg: Remote heart rate estimation using a transductive meta-learner. arXiv preprint arXiv:2007.06786.

Lee, H., Cho, A., Lee, S., and Whang, M. (2019). Vision-based measurement of heart rate from ballistocardiographic head movements using unsupervised clustering. Sensors, 19(15):3263.

Lee, K., Jin, K., Kim, Y., Lee, J. H., and Lee, E. C. (2021). A comparative analysis on the impact of face tracker and skin segmentation onto improving the performance of real-time remote photoplethysmography. In Intelligent Human Computer Interaction: 12th International Conference, IHCI 2020, Daegu, South Korea, November 24–26, 2020, Proceedings, Part II 12, pages 27–37. Springer.

Lewandowska, M., Rumiński, J., Kocejko, T., and Nowak, J. (2011). Measuring pulse rate with a webcam—a non-contact method for evaluating cardiac activity. In 2011 federated conference on computer science and information systems (FedCSIS), pages 405–410. IEEE.

Li, B., Zhang, P., Peng, J., and Fu, H. (2023). Non-contact ppg signal and heart rate estimation with multi-hierarchical convolutional network. Pattern Recognition, 139:109421.

Li, X., Chen, J., Zhao, G., and Pietikainen, M. (2014). Remote heart rate measurement from face videos under realistic situations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4264–4271.

Lin, X. and de Haan, G. (2014). Using blood volume pulse vector to extract rppg signal in infrared spectrum. Master’s thesis, Technische Universiteit Eindhoven.

Lin, Y.-C. and Lin, Y.-H. (2017). A study of color illumination effect on the snr of rppg signals. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 4301–4304. IEEE.

Liu, S., Yuen, P. C., Zhang, S., and Zhao, G. (2016a). 3d mask face anti-spoofing with remote photoplethysmography. In European Conference on Computer Vision, pages 85– 100. Springer.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016b). Ssd: Single shot multibox detector. In European conference on computer vision, pages 21–37. Springer.

Liu, Y., Jourabloo, A., Ren, W., and Liu, X. (2017). Dense face alignment. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1619–1628.

Lokendra, B. and Puneet, G. (2022). And-rppg: A novel denoising-rppg network for improv- ing remote heart rate estimation. Computers in Biology and Medicine, 141:105146.

Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M. G., Lee, J., et al. (2019). Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172.

Lv, J., Shao, X., Xing, J., Cheng, C., and Zhou, X. (2017). A deep regression architecture with two-stage re-initialization for high performance facial landmark detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3317–3326.

Martinez, L. F. C., Paez, G., and Strojnik, M. (2011). Optimal wavelength selection for noncontact reflection photoplethysmography. In 22nd Congress of the International Com- mission for Optics: Light for the Development of the World, volume 8011, page 801191. International Society for Optics and Photonics.

Matthes, K. and Hauss, W. (1938). Lichtelektrische plethysmogramme. Klinische Wochen- schrift, 17(35):1211–1213.
McDuff, D., Gontarek, S., and Picard, R. W. (2014). Improvements in remote cardiopulmonary measurement using a five band digital camera. IEEE Transactions on Biomedical Engineering, 61(10):2593–2601.

McDuff, D. J., Blackford, E. B., Estepp, J. R., and Nishidate, I. (2018). A fast non-contact imaging photoplethysmography method using a tissue-like model. In Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics, volume 10501, page 105010Q. International Society for Optics and Photonics.

Meher, J., Allende-Cid, H., and Nordling, T. E. M. (2023). A survey and classification of face alignment methods based on face models.

Meher, J., Wang, C.-C., and Nordling, T. (2022). Acquisition and synchronisation of cardiography signals from a clinical patient monitor with facial video recordings. In Pino, E., Magjarević, R., and de Carvalho, P., editors, International Conference on Biomedical and Health Informatics 2022 - Proceedings of ICBHI 2022, November 24–26, 2022, Concepcion, Chile, IFMBE Proceedings. Springer Nature.

Monitors, C. (2002). Heart rate meters, and alarms. ANSI/AAMI Standard EC13.

Morales-Fajardo, H. M., Rodríguez-Arce, J., Gutiérrez-Cedeño, A., Viñas, J. C., Reyes- Lagos, J. J., Abarca-Castro, E. A., Ledesma-Ramírez, C. I., and Vilchis-González, A. H. (2022). Towards a non-contact method for identifying stress using remote photoplethys- mography in academic environments. Sensors, 22(10):3780.

Morbiducci, U., Scalise, L., De Melis, M., and Grigioni, M. (2007). Optical vibrocardiography: A novel tool for the optical monitoring of cardiac activity. Annals of biomedical engineering, 35(1):45–58.

Mounsey, P. (1957). Praecordial ballistocardiography. British heart journal, 19(2):259.
Nakajima, K., Maekawa, T., and Miike, H. (1997). Detection of apparent skin motion using optical flow analysis: Blood pulsation signal obtained from optical flow sequence. Review of scientific instruments, 68(2):1331–1336.

Niu, X., Han, H., Shan, S., and Chen, X. (2018a). Synrhythm: Learning a deep heart rate estimator from general to specific. In 2018 24th International Conference on Pattern Recognition (ICPR), pages 3580–3585. IEEE.

Niu, X., Han, H., Shan, S., and Chen, X. (2018b). Vipl-hr: A multi-modal database for pulse estimation from less-constrained face video. In Asian Conference on Computer Vision, pages 562–576. Springer.

Niu, X., Shan, S., Han, H., and Chen, X. (2019). Rhythmnet: End-to-end heart rate estimation from face via spatial-temporal representation. IEEE Transactions on Image Processing.

Oh, T.-H., Jaroensri, R., Kim, C., Elgharib, M., Durand, F., Freeman, W. T., and Matusik, W. (2018). Learning-based video motion magnification. In Proceedings of the European Conference on Computer Vision (ECCV), pages 633–648.

Oxehealth (2022a). De novo classification request for oxehealth vital signs. https: //www.accessdata.fda.gov/cdrh_docs/reviews/DEN200019.pdf. Last accessed: 2023-11-22.

Oxehealth (2022b). Fda grants oxehealth vital signs de novo clearance; oxehealth launches in the us. https://www.oxehealth.com/news/ fda-grants-oxehealth-vital-signs-de-novo-clearance. Last accessed: 2023-11-22.

Oxehealth (2022c). Oxehealth instructions for use oxehealth vital signs. https://assets. website-files.com/5f46bcde21c863c871c74a23/6357ec8e1eb29bf9b4d80417_ Vital%20Signs-IFU-Instructions%20For%20Use-EN-GB-66.0.pdf. Last accessed: 2023-11-22.

Pilz, C. S., Blazek, V., and Leonhardt, S. (2019). On the vector space in photoplethysmog- raphy imaging. arXiv preprint arXiv:1906.04431.

Pilz, C. S., Krajewski, J., and Blazek, V. (2017). On the diffusion process for heart rate estimation from face videos under realistic conditions. In German Conference on Pattern Recognition, pages 361–373. Springer.

Pilz, C. S., Zaunseder, S., Krajewski, J., and Blazek, V. (2018). Local group invariance for heart rate estimation from face videos in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 1254–1262.

Poh, M.-Z., McDuff, D. J., and Picard, R. W. (2010a). Advancements in noncontact, multi- parameter physiological measurements using a webcam. IEEE transactions on biomedical engineering, 58(1):7–11.

Poh, M.-Z., McDuff, D. J., and Picard, R. W. (2010b). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics express, 18(10):10762–10774.

Ren, S., Cao, X., Wei, Y., and Sun, J. (2014). Face alignment at 3000 fps via regressing local binary features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1685–1692. IEEE.

Saragih, J. M., Lucey, S., and Cohn, J. F. (2011). Deformable model fitting by regularized landmark mean-shift. International journal of computer vision, 91:200–215.

Sasangohar, F., Davis, E., Kash, B. A., and Shah, S. R. (2018). Remote patient monitoring and telemedicine in neonatal and pediatric settings: Scoping literature review. Journal of medical Internet research, 20(12):e295.

seetaface (2016). Seetaface face detection and alignment. https://github.com/ seetaface/SeetaFaceEngine. Last accessed: 2023-11-22.

Shan, L. and Yu, M. (2013). Video-based heart rate measurement using head motion tracking and ica. In 2013 6th International Congress on Image and Signal Processing (CISP), volume 1, pages 160–164. IEEE.

Sinhal, R., Singh, K., and Raghuwanshi, M. (2020). An overview of remote photoplethys- mography methods for vital sign monitoring. In Computer Vision and Machine Intelli- gence in Medical Image Analysis: International Symposium, ISCMM 2019, pages 21–31. Springer.

Soleymani, M., Lichtenauer, J., Pun, T., and Pantic, M. (2011). A multimodal database for affect recognition and implicit tagging. IEEE Transactions on Affective Computing, 3(1):42–55.

Song, R., Chen, H., Cheng, J., Li, C., Liu, Y., and Chen, X. (2021). Pulsegan: Learning to generate realistic pulse waveforms in remote photoplethysmography. IEEE Journal of Biomedical and Health Informatics, 25(5):1373–1384.

Špetlík, R., Franc, V., and Matas, J. (2018). Visual heart rate estimation with convolutional neural network. In Proceedings of the British Machine Vision Conference, Newcastle, UK, pages 3–6.

Sun, Y., Azorin-Peris, V., Kalawsky, R., Hu, S., Papin, C., and Greenwald, S. E. (2012). Use of ambient light in remote photoplethysmographic systems: comparison between a high- performance camera and a low-cost webcam. Journal of biomedical optics, 17(3):037005.

Sun, Y. and Thakor, N. (2016). Photoplethysmography revisited: from contact to noncontact, from point to imaging. IEEE Transactions on Biomedical Engineering, 63(3):463–477.

Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701–1708.

Takano, C. and Ohta, Y. (2007). Heart rate measurement based on a time-lapse image. Med- ical engineering & physics, 29(8):853–857.

Tran, Q.-V., Su, S.-F., Sun, W., and Tran, M.-Q. (2019). Adaptive pulsatile plane for robust noncontact heart rate monitoring. IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Trigeorgis, G., Snape, P., Nicolaou, M. A., Antonakos, E., and Zafeiriou, S. (2016). Mnemonic descent method: A recurrent process applied for end-to-end face alignment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4177–4187.

Tsou, Y.-Y., Lee, Y.-A., and Hsu, C.-T. (2020a). Multi-task learning for simultaneous video generation and remote photoplethysmography estimation. In Proceedings of the Asian Conference on Computer Vision.

Tsou, Y.-Y., Lee, Y.-A., Hsu, C.-T., and Chang, S.-H. (2020b). Siamese-rppg network: remote photoplethysmography signal estimation from face videos. In Proceedings of the 35th Annual ACM Symposium on Applied Computing, pages 2066–2073.

Tsouri, G. R., Kyal, S., Dianat, S., and Mestha, L. (2012). Constrained independent compo- nent analysis approach to nonobtrusive pulse rate measurements. Journal of biomedical optics, 17:077011.

Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J. F., and Sebe, N. (2016). Self- adaptive matrix completion for heart rate estimation from face videos under realistic condi- tions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2396–2404.

van Gastel, M., Stuijk, S., and de Haan, G. (2015). Motion robust remote-ppg in infrared. IEEE Transactions on Biomedical Engineering, 62(5):1425–1433.

Verkruysse, W., Svaasand, L. O., and Nelson, J. S. (2008a). Remote plethysmographic imaging using ambient light. Optics express, 16(26):21434–21445.

Verkruysse, W., Svaasand, L. O., and Nelson, J. S. (2008b). Remote plethysmographic imaging using ambient light. Optics express, 16(26):21434–21445.

Viola, P. and Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57:137–154.

Vivaldy, G., Wang, C.-C., Meher, J., and Nordling, T. E. M. (2023). Protocol for collection of synchronised facial video, Electrocardiography, and Photoplethysmography data for remote Photoplethysmography model training and evaluation. Manuscript in preparation.

Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. (2013). Phase-based video motion processing. ACM Transactions on Graphics (TOG), 32(4):80.

Wadhwa, N., Rubinstein, M., Durand, F., and Freeman, W. T. (2014). Riesz pyramids for fast phase-based video magnification. In 2014 IEEE International Conference on Compu- tational Photography (ICCP), pages 1–10. IEEE.

Wang, C.-C. (2020). Non-contact heart rate measurement based on facial videos. Master’s thesis, National Cheng Kung University, No. 1, Dasyue Rd, East District, Tainan City, 701.

Wang, W., Den Brinker, A. C., and De Haan, G. (2018). Single element remote-ppg. IEEE Transactions on Biomedical Engineering.

Wang, W., Den Brinker, A. C., and De Haan, G. (2019a). Discriminative signatures for remote-ppg. IEEE Transactions on Biomedical Engineering.

Wang, W., Den Brinker, A. C., Stuijk, S., and De Haan, G. (2016). Algorithmic principles of remote ppg. IEEE Transactions on Biomedical Engineering, 64(7):1479–1491.

Wang, W., Stuijk, S., and De Haan, G. (2015a). Exploiting spatial redundancy of image sensor for motion robust rPPG. IEEE Transactions on Biomedical Engineering, 62(2):415–425.

Wang, W., Stuijk, S., and De Haan, G. (2015b). A novel algorithm for remote photo- plethysmography: Spatial subspace rotation. IEEE transactions on biomedical engineering, 63(9):1974–1984.

Wang, X., Bo, L., and Fuxin, L. (2019b). Adaptive wing loss for robust face alignment via heatmap regression. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6971–6981.

Wang, Z.-K., Kao, Y., and Hsu, C.-T. (2019c). Vision-based heart rate estimation via a two- stream cnn. In 2019 IEEE International Conference on Image Processing (ICIP), pages 3327–3331. IEEE.

Wiens, A. D., Etemadi, M., Roy, S., Klein, L., and Inan, O. T. (2014). Toward continuous, noninvasive assessment of ventricular function and hemodynamics: Wearable ballistocardiography. IEEE journal of biomedical and health informatics, 19(4):1435–1442.

Wieringa, F. P., Mastik, F., and van der Steen, A. F. (2005). Contactless multiple wavelength photoplethysmographic imaging: a first step toward “spo 2 camera"technology. Annals of biomedical engineering, 33(8):1034–1041.

Wilson, N., Guragain, B., Verma, A., Archer, L., and Tavakolian, K. (2020). Blending human and machine: feasibility of measuring fatigue through the aviation headset. Human factors, 62(4):553–564.

Wu, B.-F., Chu, Y.-W., Huang, P.-W., and Chung, M.-L. (2019a). Neural network based luminance variation resistant remote-photoplethysmography for driver's heart rate monitoring. IEEE Access, 7:57210–57225.

Wu, B.-F., Huang, P.-W., He, D.-H., Lin, C.-H., and Chen, K.-H. (2019b). Remote pho- toplethysmography enhancement with machine leaning methods. In 2019 IEEE Interna- tional Conference on Systems, Man and Cybernetics (SMC), pages 2466–2471. IEEE.

Wu, B.-F., Huang, P.-W., Lin, C.-H., Chung, M.-L., Tsou, T.-Y., and Wu, Y.-L. (2018a). Motion resistant image-photoplethysmography based on spectral peak tracking algorithm. IEEE Access, 6:21621–21634.

Wu, B.-F., Yang, Y.-Y., Tsai, B.-R., Huang, P.-W., Tsai, Y.-C., and Chen, K.-H. (2019c). Re- mote heartrate measurement based on signal feature detection in time domain. In 2019 International Conference on System Science and Engineering (ICSSE), pages 88–93. IEEE.

Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., and Freeman, W. (2012). Eulerian video magnification for revealing subtle changes in the world. ACM transactions on graphics (TOG), 31(4):1–8.

Wu, W., Qian, C., Yang, S., Wang, Q., Cai, Y., and Zhou, Q. (2018b). Look at boundary: A boundary-aware face alignment algorithm. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2129–2138.

Wu, W. and Yang, S. (2017). Leveraging intra and inter-dataset variations for robust face alignment. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 150–159.

Xiang, J. and Zhu, G. (2017). Joint face detection and facial expression recognition with mtcnn. In 2017 4th international conference on information science and control engineer- ing (ICISCE), pages 424–427. IEEE.

Xiao, S., Feng, J., Xing, J., Lai, H., Yan, S., and Kassim, A. (2016). Robust facial land- mark detection via recurrent attentive-refinement networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pages 57–72. Springer.

Xiong, X. and De la Torre, F. (2013). Supervised descent method and its applications to face alignment. IEEE transactions on pattern analysis and machine intelligence, 35(8):1888– 1902.

Yao, C., Ren, J., Bai, R., Du, H., Liu, J., and Jiang, X. (2023). Mask attack detection using vascular-weighted motion-robust rppg signals. arXiv preprint arXiv:2305.15940.

Yu, X., Laurentius, T., Bollheimer, C., Leonhardt, S., and Antink, C. H. (2020a). Noncontact monitoring of heart rate and heart rate variability in geriatric patients using photoplethysmography imaging. IEEE Journal of Biomedical and Health Informatics, 25(5):1781– 1792.

Yu, Z., Li, X., Niu, X., Shi, J., and Zhao, G. (2020b). Autohr: A strong end-to-end baseline for remote heart rate measurement with neural searching. IEEE Signal Processing Letters, 27:1245–1249.

Yu, Z., Li, X., and Zhao, G. (2019a). Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks. In Proc. BMVC.

Yu, Z., Peng, W., Li, X., Hong, X., and Zhao, G. (2019b). Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement. In Proceedings of the IEEE International Conference on Computer Vision, pages 151–160.

Zhan, Q., Wang, W., and de Haan, G. (2020). Analysis of cnn-based remote-ppg to understand limitations and sensitivities. Biomedical optics express, 11(3):1268–1283.

Zhang, J., Shan, S., Kan, M., and Chen, X. (2014a). Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment. In Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T., editors, Computer Vision – ECCV 2014, pages 1–16, Cham. Springer International Publishing.

Zhang, K., Zhang, Z., Li, Z., and Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE signal processing letters, 23(10):1499– 1503.

Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., and Li, S. Z. (2017a). Faceboxes: A cpu real-time face detector with high accuracy. In 2017 IEEE International Joint Conference on Biometrics (IJCB), pages 1–9. IEEE.

Zhang, Y., Pintea, S. L., and Van Gemert, J. C. (2017b). Video acceleration magnification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 529–537.

Zhang, Z., Luo, P., Loy, C. C., and Tang, X. (2014b). Facial landmark detection by deep multi-task learning. In European conference on computer vision, pages 94–108. Springer.

Zhang, Z., Luo, P., Loy, C. C., and Tang, X. (2015). Learning deep representation for face alignment with auxiliary attributes. IEEE transactions on pattern analysis and machine intelligence, 38(5):918–930.

Zhao, C., Lin, C.-L., Chen, W., and Li, Z. (2018). A novel framework for remote pho- toplethysmography pulse extraction on compressed videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 1299–1308.

Zhao, C., Mei, P., Xu, S., Li, Y., and Feng, Y. (2019). Performance evaluation of visual object detection and tracking algorithms used in remote photoplethysmography. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0–0.

Zhao, K., Chu, W.-S., De la Torre, F., Cohn, J. F., and Zhang, H. (2015). Joint patch and multi- label learning for facial action unit detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2207–2216.

Zheng, J., Hu, S., Chouliaras, V., and Summers, R. (2008). Feasibility of imaging pho- toplethysmography. In 2008 International Conference on BioMedical Engineering and Informatics, volume 2, pages 72–75. IEEE.

Zhou, K., Krause, S., Blocher, T., and Stork, W. (2020). Enhancing remote-ppg pulse ex- traction in disturbance scenarios utilizing spectral characteristics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 280–281.

Zhu, S., Li, C., Change Loy, C., and Tang, X. (2015). Face alignment by coarse-to-fine shape searching. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4998–5006.

Zhu, X., Lei, Z., Liu, X., Shi, H., and Li, S. Z. (2016). Face alignment across large poses: A 3d solution. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 146–155.