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An Improved Approach to the Performance of remote photoplethysmography

Computers, Materials & Continua 2022年第11期73卷 2773-2783页

作者： Yi Sheng Wu Zeng Qiuyu Hu Weihua Ou Yuxuan Xie Jie Li School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430000China School of Electronic and Information Engineering Guizhou Normal UniversityGuizhou550000China Gina Cody School of Engineering and Computer Science Concordia UniversityW.MontrealQuebecH3G1M8Canada

Heart rate is an important metric for determining physical and mental *** recent years,remote photoplethysmography(rPPG)has been widely used in characterizing physiological signals in human ***,research on non-contact detection of heart rate mainly focuses on the capture and separation of spectral signals from video ***,this method is very sensitive to the movement of the test subject and light intensity variation,and this results in motion artifacts which presents challenges in extracting accurate physiological signals such as heart *** this paper,an improved method for rPPG signal preprocessing is *** on the well known red green blue(RGB)color space,we segmented skin tone in different color spaces and extracted rPPG signals,after which we use a skin segmentation training model based on the luminance component,the blue-difference chroma components,and red-difference chroma components(YCbCr),as well as hue saturation intensity(HSI)color *** the experimental verification section,we compare the robustness of the signal on different color *** summary,we are experimentally verifying a better image pre-processing method based on real-time rPPG,which results in more precise measurements through the comparative analysis of skin segmentation and signal quality.

关键词： remote photoplethysmography skin segmentation heart rate

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A Deepfake Detection Algorithm Based on Fourier Transform of Biological Signal

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Computers, Materials & Continua 2024年第6期79卷 5295-5312页

作者： Yin Ni Wu Zeng Peng Xia Guang Stanley Yang Ruochen Tan School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430023China School of Mathematics and Computer Science Wuhan Polytechnic UniversityWuhan430048China Paul G.Allen School of Computer Science and Engineering University ofWashingtonSeattleWA98195USA School of Computer Science and Engineering University of CaliforniaSanDiegoCA92093USA

Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal *** current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac *** this gap,many researchers have developed detection methods focusing on biometric *** methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection ***,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac *** introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification ***,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention *** experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.

关键词： Deepfake detector remote photoplethysmography fast fourier transform spatial attention mechanism

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Video-Based Deception Detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

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Journal of Beijing Institute of Technology 2024年第3期33卷 175-185页

作者： Yanfeng Li Jincheng Bian Yiqun Gao Rencheng Song Department of Biomedical Engineering Hefei University of TechnologyHefei 230009China

Deception detection plays a crucial role in criminal *** contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception *** this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)*** wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception *** evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)*** results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for *** demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.

关键词： deception detection apparent visual features remote photoplethysmography non-contact heart rate feature selection

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MSSTNet:Multi-scale facial videos pulse extraction network based on separable spatiotemporal convolution and dimension separable attention

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Virtual Reality & Intelligent Hardware 2023年第2期5卷 124-141页

作者： Changchen ZHAO Hongsheng WANG Yuanjing FENG College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China Hangzhou Innovation Institute Beihang UniversityHangzhou 310053China

Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent *** methods are primarily based on a singlescale region of interest(ROI).However,some noise signals that are not easily separated in a single-scale space can be easily separated in a multi-scale ***,existing spatiotemporal networks mainly focus on local spatiotemporal information and do not emphasize temporal information,which is crucial in pulse extraction problems,resulting in insufficient spatiotemporal feature *** Here,we propose a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution(SSTC)and dimension separable attention(DSAT).First,to solve the problem of a single-scale ROI,we constructed a multi-scale feature space for initial signal ***,SSTC and DSAT were designed for efficient spatiotemporal correlation modeling,which increased the information interaction between the long-span time and space dimensions;this placed more emphasis on temporal *** The signal-to-noise ratio(SNR)of the proposed network reached 9.58dB on the PURE dataset and 6.77dB on the UBFC-rPPG dataset,outperforming state-of-the-art *** The results showed that fusing multi-scale signals yielded better results than methods based on only single-scale *** proposed SSTC and dimension-separable attention mechanism will contribute to more accurate pulse signal extraction.

关键词： remote photoplethysmography Heart rate Separable spatiotemporal convolution Dimension separable attention Multi-scale Neural network

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In vivo skin imaging prototypes ＂made in Latvia＂

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Frontiers of Optoelectronics 2017年第3期10卷 255-266页

作者： Janis SPIGULIS Biophotonics Laboratory Institute of Atomic Physics and Spectroscopy University of Latvia Riga LV-1586 Latvia

This paper briefly reviews the operational principles and designs of portable in vivo skin imaging prototypes developed at the Biophotonics Laboratory of the Institute of Atomic Physics and Spectroscopy, University of Latvia. Four types of imaging devices are presented. Multi-spectral imagers ensure distant mapping of specific skin parameters （e.g., distribution of skin chromophores）. Autofluorescence photobleaching rate imagers show potential for skin tumor assessment and margin delineation. photoplethysmography video-imagers remotely detect cutaneous blood pulsations and provide real-time information on the human cardiovascular state. Multimodal skin imagers perform the above-mentioned functions by acquiring several spectral and video images using the same image sensor.

关键词： multispectral skin imaging autofluorescence photobleaching remote photoplethysmography

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