Analysis of Aliasing-Induced Heart-Rate Lock-in in Deep rPPG Models under Controlled Periodic Illumination Flicker

doi:10.22716/sckt.2026.14.3.011

All Issue

2026 Vol.14, Issue 3 Preview Page Next Page

Research Article

Analysis of Aliasing-Induced Heart-Rate Lock-in in Deep rPPG Models under Controlled Periodic Illumination Flicker 통제된 주기적 조명 깜박임 조건에서 심층 rPPG 모델의 에일리어싱에 의한 심박수 고착 분석: Jeongun Jin¹, Jihye Kim², Joonhyun Jeon³, Jeongung Kim⁴, and Soonchul Kwon⁵
진정운¹, 김지혜², 전준현³, 김정웅⁴, 권순철⁵; ¹Master’s Student, Dept. of Interdisciplinary Information System, Kwangwoon University
²Chief Executive Officer, IEUMSOOP Incorporated Association
³Ph.D. Student, Dept. of Plasma Bio Display, Kwangwoon University
⁴Ph.D. Student, Dept. of Plasma Bio Display, Kwangwoon University
⁵Associate Professor, Dept. of Interdisciplinary Information System, Kwangwoon University

¹광운대학교 정보융합시스템학과 석사과정, ²사단법인 이음숲 대표, ³광운대학교 플라즈마바이오디스플레이학과 박사과정,
⁴광운대학교 플라즈마바이오디스플레이학과 박사과정, ⁵광운대학교 정보융합시스템학과 부교수

30 September 2026. pp. 145-163

PDF

Abstract

원격 광용적맥파(remote photoplethysmography, rPPG)는 카메라 영상만으로 심박수를 비접촉 추정하지만, 유한한 프레임률로 영상을 표본화하므로 주기적 조명 성분이 심박 대역의 에일리어싱 성분으로 관측될 수 있다. 본 논문은 실제 조명 환경에서의 빈번한 발생을 주장하는 것이 아니라, 통제된 합성 조명 깜박임 조건에서 에일리어싱 성분이 심층 rPPG 모델 출력에 미치는 영향을 분석한다. 30 fps 조건에서 29, 31.5, 32, 33 Hz의 조명 깜박임이 각각 60, 90, 120, 180 BPM의 가짜 심박수로 관측되었으며, DeepPhys, TSCAN, EfficientPhys와 VIPL-HR, PURE 데이터셋에서 심박수 고착 현상이 일관되게 관찰되었다. 에일리어싱 출력 검출을 위해 확장 고조파 비율(Extended Harmonic Ratio, EHR)을 정의하여 VIPL-HR과 PURE에서 각각 AUC 0.930과 0.958을 얻었다. 원본 데이터 분석에서는 명확한 자연조명 에일리어싱이 관찰되지 않았으며, 일부 오탐지는 고조파 혼동으로 설명되었다. 가짜 심박수 고착으로 판정된 표본을 대상으로 한 보조적 완화에서는, 조명 주파수에 대한 사전 정보 없이 후보 peak를 재선택하여 잔여 고착 비율을 13%로 낮추었다.

Remote photoplethysmography (rPPG) enables contactless heart-rate estimation from facial videos. Since cameras sample at a finite frame rate, periodic illumination components can be folded into the heart-rate band through aliasing, causing deep rPPG models to output a fake heart rate. Rather than claiming this occurs frequently in real environments, we analyze its effect under controlled synthetic flicker. Under 30 fps sampling, flickers at 29, 31.5, 32, and 33 Hz fold into fake rates of 60, 90, 120, and 180 BPM, and this lock-in was consistently observed across DeepPhys, TSCAN, and EfficientPhys on VIPL-HR and PURE. The proposed Extended Harmonic Ratio (EHR) detected aliased outputs with AUC scores of 0.930 and 0.958 on the two datasets. A clean-data control found no clear natural aliasing, with false positives explained by harmonic confusion. On samples identified as fake-HR lock-in, an auxiliary mitigation using candidate peak reselection—without prior knowledge of the flicker frequency—reduced the residual lock-in rate to 13%.

Keywords

Remote photoplethysmography

rPPG

Illumination flicker

Aliasing

Heart-rate lock-in

Extended harmonic ratio

References

W. Verkruysse, L. O. Svaasand, and J. S. Nelson, “Remote plethysmographic imaging using ambient light,” Optics Express, Vol. 16, No. 26, pp. 21434-21445, 2008.
10.1364/OE.16.021434
G. de Haan and V. Jeanne, “Robust pulse rate from chrominance-based rPPG,” IEEE Transactions on Biomedical Engineering, Vol. 60, No. 10, pp. 2878-2886, 2013.
10.1109/TBME.2013.2266196
W. Wang, A. C. den Brinker, S. Stuijk, and G. de Haan, “Algorithmic principles of remote PPG,” IEEE Transactions on Biomedical Engineering, Vol. 64, No. 7, pp. 1479-1491, 2017.
10.1109/TBME.2016.2609282
W. Chen and D. McDuff, “DeepPhys: Video-based physiological measurement using convolutional attention networks,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 349-365, 2018.
10.1007/978-3-030-01216-8_22
X. Liu, J. Fromm, S. Patel, and D. McDuff, “Multi-task temporal shift attention networks for on-device contactless vitals measurement,” in Advances in Neural Information Processing Systems (NeurIPS), Vol. 33, pp. 19400-19411, 2020.
Z. Yu, W. Peng, X. Li, X. Hong, and G. Zhao, “Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks,” in Proceedings of the British Machine Vision Conference (BMVC), 2019.
Z. Yu, Y. Shen, J. Shi, H. Zhao, P. Torr, and G. Zhao, “PhysFormer: Facial video-based physiological measurement with temporal difference transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4186-4196, 2022.
10.1109/CVPR52688.2022.00415
X. Liu, B. Hill, Z. Jiang, S. Patel, and D. McDuff, “EfficientPhys: Enabling simple, fast and accurate camera-based cardiac measurement,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 5008-5017, 2023.
10.1109/WACV56688.2023.00498
A. V. Oppenheim and R. W. Schafer, Discrete-Time Signal Processing, 3rd ed. Pearson, 2010.
L. Tarassenko, M. Villarroel, A. Guazzi, J. Jorge, D. A. Clifton, and C. Pugh, “Non-contact video-based vital sign monitoring using ambient light and auto-regressive models,” Physiological Measurement, Vol. 35, No. 5, pp. 807-831, 2014.
10.1088/0967-3334/35/5/807
Z. Yang, H. Wang, and F. Lu, “Assessment of deep learning-based heart rate estimation using remote photoplethysmography under different illuminations,” IEEE Transactions on Human-Machine Systems, Vol. 52, No. 6, pp. 1236-1246, 2022.
10.1109/THMS.2022.3207755
S. Chen, S. K. Ho, J. W. Chin, K. H. Luo, T. T. Chan, R. H. Y. So, and K. L. Wong, “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 Workshops (CVPRW), pp. 6077-6085, 2023.
10.1109/CVPRW59228.2023.00647
B. Acharya, W. Saakyan, B. Hammer, and H. Drimalla, “The reliability of remote photoplethysmography under low illumination and elevated heart rates,” npj Digital Medicine, Vol. 8, article 744, 2025.
10.1038/s41746-025-02192-y 41339699 PMC12678791
X. Niu, H. Han, S. Shan, and X. Chen, “VIPL-HR: A multi-modal database for pulse estimation from less-constrained face video,” in Proceedings of the Asian Conference on Computer Vision (ACCV), pp. 562-576, 2018.
10.1007/978-3-030-20873-8_36
R. Stricker, S. Müller, and H. Gross, “Non-contact video-based pulse rate measurement on a mobile service robot,” in Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1056-1062, 2014.
10.1109/ROMAN.2014.6926392

Information

Publisher :The Society of Convergence Knowledge
Publisher(Ko) :융복합지식학회
Journal Title :The Society of Convergence Knowledge Transactions
Journal Title(Ko) :융복합지식학회논문지
Volume : 14
No :3
Pages :145-163
DOI :https://doi.org/10.22716/sckt.2026.14.3.011

[1] W. Verkruysse, L. O. Svaasand, and J. S. Nelson, “Remote plethysmographic imaging using ambient light,” Optics Express, Vol. 16, No. 26, pp. 21434-21445, 2008.
10.1364/OE.16.021434

[2] G. de Haan and V. Jeanne, “Robust pulse rate from chrominance-based rPPG,” IEEE Transactions on Biomedical Engineering, Vol. 60, No. 10, pp. 2878-2886, 2013.
10.1109/TBME.2013.2266196

[3] W. Wang, A. C. den Brinker, S. Stuijk, and G. de Haan, “Algorithmic principles of remote PPG,” IEEE Transactions on Biomedical Engineering, Vol. 64, No. 7, pp. 1479-1491, 2017.
10.1109/TBME.2016.2609282

[4] W. Chen and D. McDuff, “DeepPhys: Video-based physiological measurement using convolutional attention networks,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 349-365, 2018.
10.1007/978-3-030-01216-8_22

[5] X. Liu, J. Fromm, S. Patel, and D. McDuff, “Multi-task temporal shift attention networks for on-device contactless vitals measurement,” in Advances in Neural Information Processing Systems (NeurIPS), Vol. 33, pp. 19400-19411, 2020.

[6] Z. Yu, W. Peng, X. Li, X. Hong, and G. Zhao, “Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks,” in Proceedings of the British Machine Vision Conference (BMVC), 2019.

[7] Z. Yu, Y. Shen, J. Shi, H. Zhao, P. Torr, and G. Zhao, “PhysFormer: Facial video-based physiological measurement with temporal difference transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4186-4196, 2022.
10.1109/CVPR52688.2022.00415

[8] X. Liu, B. Hill, Z. Jiang, S. Patel, and D. McDuff, “EfficientPhys: Enabling simple, fast and accurate camera-based cardiac measurement,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 5008-5017, 2023.
10.1109/WACV56688.2023.00498

[9] A. V. Oppenheim and R. W. Schafer, Discrete-Time Signal Processing, 3rd ed. Pearson, 2010.

[10] L. Tarassenko, M. Villarroel, A. Guazzi, J. Jorge, D. A. Clifton, and C. Pugh, “Non-contact video-based vital sign monitoring using ambient light and auto-regressive models,” Physiological Measurement, Vol. 35, No. 5, pp. 807-831, 2014.
10.1088/0967-3334/35/5/807

[11] Z. Yang, H. Wang, and F. Lu, “Assessment of deep learning-based heart rate estimation using remote photoplethysmography under different illuminations,” IEEE Transactions on Human-Machine Systems, Vol. 52, No. 6, pp. 1236-1246, 2022.
10.1109/THMS.2022.3207755

[12] S. Chen, S. K. Ho, J. W. Chin, K. H. Luo, T. T. Chan, R. H. Y. So, and K. L. Wong, “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 Workshops (CVPRW), pp. 6077-6085, 2023.
10.1109/CVPRW59228.2023.00647

[13] B. Acharya, W. Saakyan, B. Hammer, and H. Drimalla, “The reliability of remote photoplethysmography under low illumination and elevated heart rates,” npj Digital Medicine, Vol. 8, article 744, 2025.
10.1038/s41746-025-02192-y 41339699 PMC12678791

[14] X. Niu, H. Han, S. Shan, and X. Chen, “VIPL-HR: A multi-modal database for pulse estimation from less-constrained face video,” in Proceedings of the Asian Conference on Computer Vision (ACCV), pp. 562-576, 2018.
10.1007/978-3-030-20873-8_36

[15] R. Stricker, S. Müller, and H. Gross, “Non-contact video-based pulse rate measurement on a mobile service robot,” in Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1056-1062, 2014.
10.1109/ROMAN.2014.6926392

The Society of Convergence Knowledge Transactions ISSN:2287-8920(Print) 융복합지식학회논문지

All Issue