Siamese-rPPG Network: Remote Photoplethysmography Signal Estimation from Face Videos

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The document presents the Siamese-rPPG Network for estimating remote photoplethysmography (rPPG) signals from face videos. The network uses a Siamese architecture with 3D convolutional layers and weight sharing to learn rPPG signals from two facial regions simultaneously. Evaluation on three datasets shows the network outperforms existing methods for contactless heart rate estimation from video in terms of correlation and error metrics.

Science

Siamese-rPPG Network:
Remote Photoplethysmography Signal
Estimation from Face Videos
Yun-Yun Tsou, Yi-An Lee, Chiou-Ting Hsu and Shang-Hung Chang
National Tsing Hua University
Linkou Chang Gung Memorial Hospital

•With specific contact devices
•Electrocardiography (ECG) and Photoplenthesmography
• Disadvantages
• Require specific devices and professional attention
• Hardly extend to mass monitoring for a large group of subjects
Heart rate estimation

•Contactless video-based methods
•Remote photoplethysmography (rPPG)
• Analyze blood volume changes in optical information
Heart rate estimation
PPG signal
ECG signal
70 bpm

•Contactless video-based methods
•Traditional method
• Vulnerable to environmental interference and subjects’ motion
• Performance heavily depends on the quality of videos
•Learning-based method
• Require image pre-processing step
• Impractical for real-world applications
Heart rate estimation

•Goal:
•Estimate rPPG signals directly from input video sequences
• Siamese-rPPG network
• Simultaneously learn rPPG signals from two face regions
• 3D convolutional layers
• Characterize the spatial and temporal information of the videos
Introduction

[2] Ying Qiu, Yang Liu, Juan Arteaga-Falconi, Haiwei Dong, and Abdulmotaleb E. Saddik. 2019. EVM-CNN: Real-Time Contactless Heart Rate Estimation From Facial Video. IEEE Transactions on Multimedia21
, 7 (July 2019), 1778–1787
Related work: EVM-CNN
•EVM-CNN: Real-Time Contactless Heart Rate Estimation From Facial Video [2]

[1] Radim Spetlík, Vojtêch Franc, Jan Čech, and Jiří Matas 2018. Visual Heart Rate Estimation with Convolutional Neural Network. In Proceedings of British Machine Vision Conference
Related work: HR-CNN
•Visual Heart Rate Estimation with Convolutional Neural Network [1]

•Siamese-rPPG network
•Face detector
• Locate two regions of interest (ROIs)
•3D convolutional layers
• Directly process the located videos
•Two weight sharing networks
• Mutually learn the characteristic information from two ROIs
Our approach

Our approach
•Siamese-rPPG network
•Weight-sharing network
• Heterogeneous features
• Each branch learn the characteristic features of its corresponding region
• Homogeneous features
• Capture the homogeneity from two different facial regions

•Siamese-rPPG network
•Loss function
• Pearson correlation
• Negative Pearson loss
Our approach

•COHFACE dataset
• 160 heavily compressed videos from 40 subjects
• Training set contains 24 subjects and the testing set contains 16 subjects
•UBFC-RPPG dataset
• 42 videos from 42 subjects
• The training set contains 28 subjects and testing set contains 14 subjects
•PURE dataset
• 60 videos from 10 subjects
• The training set contains 7 subjects and testing set contains 3 subjects
11
Dataset

•Evaluation measurement
•Pearson correlation coefficient (R)
•Mean absolute error (MAE)
12
•Root mean square error (RMSE)
•Precision at 2.5 or 5 bpm
Experiment

•Ablation study on COHFACE dataset
13
Experiment

•Results for concatenation and addition
14
Experiment
(a) Concatenation (b) Element-wise addition

•Comparison on COHFACE dataset
15
Experiment
(a) Controlled scenario
(b) Natural scenario

•Comparison on UBFC-RPPG dataset
16
Experiment

•Comparison of cross-dataset estimation
Experiment

•Propose Siamese-rPPG network to estimate rPPG from facial videos
•3D convolutional layers
• Model the spatial and temporal characteristics of rPPG signals
•Weight-sharing mechanism
• Learn robust, distinctive, and complementary features from multiple facial regions
•Outperform all the existing methods
19
Conclusion

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Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...

Sérgio Sacani

We present the JWST discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS + 53.13485 − 27.82088 with a host spectroscopic redshift of 2.903 ± 0.007 . The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red ( � ⁢ ( � − � ) ∼ 0.9 ) despite a host galaxy with low-extinction and has a high Ca II velocity ( 19 , 000 ± 2 , 000 km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low- � Ca-rich population. Although such an object is too red for any low- � cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement ( ≲ 1 ⁢ � ) with Λ CDM. Therefore unlike low- � Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high- � truly diverge from their low- � counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.

Analysis of Polygenic Traits (GPB-602)

PABOLU TEJASREE

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Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...

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Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.

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PirithiRaju

Insectsplayamajorroleinthedeteriorationoffoodgrainscausingbothquantitativeandqualitativelosses Wellprovedthatnogranariescanbefilledwithgrainswithoutinsectsastheharvestedproducecontainegg(or)larvae(or)pupae(or)adultinsectinthembecauseoffieldcarryoverinfestationwhichcannotbeavoidedindevelopingcountrieslikeIndia Simpletechnologiesfortimelydetectionofinsectsinthestoredproduceandtherebyplantimelycontrolmeasures

一比一原版美国佩斯大学毕业证如何办理

gyhwyo

原版一模一样【微信：741003700 】【美国佩斯大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 【主营项目】一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才办理美国佩斯大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。办理美国佩斯大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：校徽：象征着学校的荣誉和传承。校名:学校英文全称授予学位：本部分将注明获得的具体学位名称。毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。办理美国佩斯大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。综上所述，办理美国佩斯大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

Explainable Deepfake Image/Video Detection

VasileiosMezaris

Presentation of our paper, "Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection", by K. Tsigos, E. Apostolidis, S. Baxevanakis, S. Papadopoulos, V. Mezaris. Presented at the ACM Int. Workshop on Multimedia AI against Disinformation (MAD’24) of the ACM Int. Conf. on Multimedia Retrieval (ICMR’24), Thailand, June 2024. https://doi.org/10.1145/3643491.3660292 https://arxiv.org/abs/2404.18649 Software available at https://github.com/IDT-ITI/XAI-Deepfakes

2001_Book_HumanChromosomes - Genéticapdf

lucianamillenium

Mites,Slug,Snail_Infesting agricultural crops.pdf

PirithiRaju

Order : Trombidiformes (Acarina) Class : Arachnida Mites normally feed on the undersurface of the leaves but the symptoms are more easily seen on the uppersurface. Tetranychids produce blotching (Spots) on the leaf-surface. Tarsonemids and Eriophyids produce distortion (twist), puckering (Folds) or stunting (Short) of leaves. Eriophyids produce distinct galls or blisters (fluid-filled sac in the outer layer)

Firoozeh Kashani-Sabet - An Esteemed Professor

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Siamese-rPPG Network: Remote Photoplethysmography Signal Estimation from Face Videos

1. Siamese-rPPG Network: Remote Photoplethysmography Signal Estimation from Face Videos Yun-Yun Tsou, Yi-An Lee, Chiou-Ting Hsu and Shang-Hung Chang National Tsing Hua University Linkou Chang Gung Memorial Hospital

2. •With specific contact devices •Electrocardiography (ECG) and Photoplenthesmography • Disadvantages • Require specific devices and professional attention • Hardly extend to mass monitoring for a large group of subjects Heart rate estimation

3. •Contactless video-based methods •Remote photoplethysmography (rPPG) • Analyze blood volume changes in optical information Heart rate estimation PPG signal ECG signal 70 bpm

4. •Contactless video-based methods •Traditional method • Vulnerable to environmental interference and subjects’ motion • Performance heavily depends on the quality of videos •Learning-based method • Require image pre-processing step • Impractical for real-world applications Heart rate estimation

5. •Goal: •Estimate rPPG signals directly from input video sequences • Siamese-rPPG network • Simultaneously learn rPPG signals from two face regions • 3D convolutional layers • Characterize the spatial and temporal information of the videos Introduction

6. [2] Ying Qiu, Yang Liu, Juan Arteaga-Falconi, Haiwei Dong, and Abdulmotaleb E. Saddik. 2019. EVM-CNN: Real-Time Contactless Heart Rate Estimation From Facial Video. IEEE Transactions on Multimedia21 , 7 (July 2019), 1778–1787 Related work: EVM-CNN •EVM-CNN: Real-Time Contactless Heart Rate Estimation From Facial Video [2]

7. [1] Radim Spetlík, Vojtêch Franc, Jan Čech, and Jiří Matas 2018. Visual Heart Rate Estimation with Convolutional Neural Network. In Proceedings of British Machine Vision Conference Related work: HR-CNN •Visual Heart Rate Estimation with Convolutional Neural Network [1]

8. •Siamese-rPPG network •Face detector • Locate two regions of interest (ROIs) •3D convolutional layers • Directly process the located videos •Two weight sharing networks • Mutually learn the characteristic information from two ROIs Our approach

9. Our approach •Siamese-rPPG network •Weight-sharing network • Heterogeneous features • Each branch learn the characteristic features of its corresponding region • Homogeneous features • Capture the homogeneity from two different facial regions

10. •Siamese-rPPG network •Loss function • Pearson correlation • Negative Pearson loss Our approach

11. •COHFACE dataset • 160 heavily compressed videos from 40 subjects • Training set contains 24 subjects and the testing set contains 16 subjects •UBFC-RPPG dataset • 42 videos from 42 subjects • The training set contains 28 subjects and testing set contains 14 subjects •PURE dataset • 60 videos from 10 subjects • The training set contains 7 subjects and testing set contains 3 subjects 11 Dataset

12. •Evaluation measurement •Pearson correlation coefficient (R) •Mean absolute error (MAE) 12 •Root mean square error (RMSE) •Precision at 2.5 or 5 bpm Experiment

13. •Ablation study on COHFACE dataset 13 Experiment

14. •Results for concatenation and addition 14 Experiment (a) Concatenation (b) Element-wise addition

15. •Comparison on COHFACE dataset 15 Experiment (a) Controlled scenario (b) Natural scenario

16. •Comparison on UBFC-RPPG dataset 16 Experiment

17. •Comparison on PURE dataset Experiment

18. •Comparison of cross-dataset estimation Experiment

19. •Propose Siamese-rPPG network to estimate rPPG from facial videos •3D convolutional layers • Model the spatial and temporal characteristics of rPPG signals •Weight-sharing mechanism • Learn robust, distinctive, and complementary features from multiple facial regions •Outperform all the existing methods 19 Conclusion

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