Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of decepti...Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In 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)technique.Multiple 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 detection.We 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)dataset.Experimental 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 BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.展开更多
Heart rate is an important vital characteristic which indicates physical and mental health status.Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly.Th...Heart rate is an important vital characteristic which indicates physical and mental health status.Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly.Therefore,the study of non-contact heart rate measurement methods is of great importance.Based on the principles of photoelectric volumetric tracing,we use a computer device and camera to capture facial images,accurately detect face regions,and to detect multiple facial images using a multi-target tracking algorithm.Then after the regional segmentation of the facial image,the signal acquisition of the region of interest is further resolved.Finally,frequency detection of the collected Photo-plethysmography(PPG)and Electrocardiography(ECG)signals is completed with peak detection,Fourier analysis,and a Waveletfilter.The experimental results show that the subject’s heart rate can be detected quickly and accurately even when monitoring multiple facial targets simultaneously.展开更多
Heart rate is an important data reflecting human vital characteristics and an important reference index to describe human physical and mental state.Currently,widely used heart rate measurement devices require direct c...Heart rate is an important data reflecting human vital characteristics and an important reference index to describe human physical and mental state.Currently,widely used heart rate measurement devices require direct contact with a person’s skin,which is not suitable for people with burns,delicate skin,newborns and the elderly.Therefore,the research of non-contact heart rate measurement method is of great significance.Based on the basic principle of Photoplethysmography,we use the camera of computer equipment to capture the face image,detect the face region accurately,and detect multiple faces in the image based on multi-target tracking algorithm.Then the region segmentation of the face image is carried out to further realize the signal acquisition of the region of interest.Finally,peak detection,Fourier analysis and wavelet analysis were used to detect the frequency of PPG and ECG signals.The experimental results show that the heart rate information can be quickly and accurately detected even in the case of monitoring multiple face targets.展开更多
Image photoplethysmography can realize low-cost and easy-to-operate non-contact heart rate detection from the facial video, and effectively overcome the limitations of traditional contact method in daily vital sign mo...Image photoplethysmography can realize low-cost and easy-to-operate non-contact heart rate detection from the facial video, and effectively overcome the limitations of traditional contact method in daily vital sign monitoring. However, it is hard to obtain more accurate heart rate detection values under the conditions of subject’s facial movement, weak ambient light intensity and long detection distance, etc. In this article, a non-contact heart rate detection method based on face tracking is proposed, which can effectively improve the accuracy of non-contact heart rate detection method in practical application. The corner tracker algorithm is used to track the human face to reduce the motion artifact caused by the movement of the subject’s face and enhance the use value of the signal. And the maximum ratio combining algorithm is used to weight the pixel space pulse wave signal in the facial region of interest to improve the pulse wave extraction accuracy. We analyzed the facial images collected under different experimental distances and action states. This proposed method significantly reduces the error rate compared with the independent component analysis method. After theoretical analysis and experimental verification, this method effectively reduces the error rate under different experimental variables and has good consistency with the heart rate value collected by the medical physiological vest. This method will help to improve the accuracy of non-contact heart rate detection in complex environments.展开更多
Previous studies have found that drivers’physiological conditions can deteriorate under noise conditions,which poses a potential hazard when driving.As a result,it is crucial to identify the status of drivers when ex...Previous studies have found that drivers’physiological conditions can deteriorate under noise conditions,which poses a potential hazard when driving.As a result,it is crucial to identify the status of drivers when exposed to different noises.However,such explo-rations are rarely discussed with short-term physiological indicators,especially for rail transit drivers.In this study,an experiment involving 42 railway transit drivers was conducted with a driving simulator to assess the impact of noise on drivers’physiological responses.Considering the individuals’heterogeneity,this study introduced drivers’noise annoyance to measure their self-noise-adaption.The variances of drivers’heart rate variability(HRV)along with different noise adaptions are explored when exposed to different noise conditions.Several machine learning approaches(support vector machine,K-nearest neighbour and random forest)were then used to classify their physiological status under different noise conditions according to the HRV and drivers’self-noise adaptions.Results indicate that the volume of traffic noise negatively affects drivers’performance in their routines.Drivers with different noise adaptions but exposed to a fixed noise were found with discrepant HRV,demonstrating that noise adaption is highly associated with drivers’physiological status under noises.It is also found that noise adaption inclusion could raise the accuracy of classifications.Overall,the random forests classifier performed the best in identifying the physiological status when exposed to noise conditions for drivers with different noise adaptions.展开更多
心率和血氧饱和度是反映人体健康状况极其重要的生理指标.近年来,基于成像式光电容积描记技术(imaging photoplethysmography,IPPG)的非接触式心率和血氧饱和度检测方法因为其方便快捷且受约束较少等优点开始逐步成为研究热点.主要工作...心率和血氧饱和度是反映人体健康状况极其重要的生理指标.近年来,基于成像式光电容积描记技术(imaging photoplethysmography,IPPG)的非接触式心率和血氧饱和度检测方法因为其方便快捷且受约束较少等优点开始逐步成为研究热点.主要工作如下:首先,介绍了非接触式检测方法的背景和研究意义;其次,从目标区域检测和感兴趣区域(region of interest,ROI)选取两个方面总结并点明其研究现状以及未来改进方向;再次,从传统方法、信号处理结合深度学习方法以及端到端方法3个方面对心率和血氧饱和度检测方法进行了总结,并梳理了深度学习方法所使用的数据集以及在各个数据集中所展现的检测效果;最后,指出该领域所存在的亟待解决的问题以及未来的研究方向.展开更多
基金National Natural Science Foundation of China(No.62271186)Anhui Key Project of Research and Development Plan(No.202104d07020005)。
文摘Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In 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)technique.Multiple 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 detection.We 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)dataset.Experimental 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 BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.
基金supported by the National Nature Science Foundation of China(Grant Number:61962010).
文摘Heart rate is an important vital characteristic which indicates physical and mental health status.Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly.Therefore,the study of non-contact heart rate measurement methods is of great importance.Based on the principles of photoelectric volumetric tracing,we use a computer device and camera to capture facial images,accurately detect face regions,and to detect multiple facial images using a multi-target tracking algorithm.Then after the regional segmentation of the facial image,the signal acquisition of the region of interest is further resolved.Finally,frequency detection of the collected Photo-plethysmography(PPG)and Electrocardiography(ECG)signals is completed with peak detection,Fourier analysis,and a Waveletfilter.The experimental results show that the subject’s heart rate can be detected quickly and accurately even when monitoring multiple facial targets simultaneously.
文摘Heart rate is an important data reflecting human vital characteristics and an important reference index to describe human physical and mental state.Currently,widely used heart rate measurement devices require direct contact with a person’s skin,which is not suitable for people with burns,delicate skin,newborns and the elderly.Therefore,the research of non-contact heart rate measurement method is of great significance.Based on the basic principle of Photoplethysmography,we use the camera of computer equipment to capture the face image,detect the face region accurately,and detect multiple faces in the image based on multi-target tracking algorithm.Then the region segmentation of the face image is carried out to further realize the signal acquisition of the region of interest.Finally,peak detection,Fourier analysis and wavelet analysis were used to detect the frequency of PPG and ECG signals.The experimental results show that the heart rate information can be quickly and accurately detected even in the case of monitoring multiple face targets.
文摘Image photoplethysmography can realize low-cost and easy-to-operate non-contact heart rate detection from the facial video, and effectively overcome the limitations of traditional contact method in daily vital sign monitoring. However, it is hard to obtain more accurate heart rate detection values under the conditions of subject’s facial movement, weak ambient light intensity and long detection distance, etc. In this article, a non-contact heart rate detection method based on face tracking is proposed, which can effectively improve the accuracy of non-contact heart rate detection method in practical application. The corner tracker algorithm is used to track the human face to reduce the motion artifact caused by the movement of the subject’s face and enhance the use value of the signal. And the maximum ratio combining algorithm is used to weight the pixel space pulse wave signal in the facial region of interest to improve the pulse wave extraction accuracy. We analyzed the facial images collected under different experimental distances and action states. This proposed method significantly reduces the error rate compared with the independent component analysis method. After theoretical analysis and experimental verification, this method effectively reduces the error rate under different experimental variables and has good consistency with the heart rate value collected by the medical physiological vest. This method will help to improve the accuracy of non-contact heart rate detection in complex environments.
基金supported by the Sichuan Mineral Resources Research Center(Gr ant No.SCKCZY2023-ZC010)the Gansu Tec h-nological Innovation Guidance Plan(Grant No.22CX8JA142)+2 种基金the Sc hool Enter prise Cooperation Program of Southwest Jiao-tong Univ ersity(Grant No.LG-YY-CW-2020010)the Open Fund of Key Laboratory of Flight Techniques and Flight Safety(Grant No.FZ2021KF05)the Key Research Base of Humanistic and Social Sciences of Deyang-Psychology and Behavior Science Research Center(Grant No.XLYXW2023202).
文摘Previous studies have found that drivers’physiological conditions can deteriorate under noise conditions,which poses a potential hazard when driving.As a result,it is crucial to identify the status of drivers when exposed to different noises.However,such explo-rations are rarely discussed with short-term physiological indicators,especially for rail transit drivers.In this study,an experiment involving 42 railway transit drivers was conducted with a driving simulator to assess the impact of noise on drivers’physiological responses.Considering the individuals’heterogeneity,this study introduced drivers’noise annoyance to measure their self-noise-adaption.The variances of drivers’heart rate variability(HRV)along with different noise adaptions are explored when exposed to different noise conditions.Several machine learning approaches(support vector machine,K-nearest neighbour and random forest)were then used to classify their physiological status under different noise conditions according to the HRV and drivers’self-noise adaptions.Results indicate that the volume of traffic noise negatively affects drivers’performance in their routines.Drivers with different noise adaptions but exposed to a fixed noise were found with discrepant HRV,demonstrating that noise adaption is highly associated with drivers’physiological status under noises.It is also found that noise adaption inclusion could raise the accuracy of classifications.Overall,the random forests classifier performed the best in identifying the physiological status when exposed to noise conditions for drivers with different noise adaptions.
文摘心率和血氧饱和度是反映人体健康状况极其重要的生理指标.近年来,基于成像式光电容积描记技术(imaging photoplethysmography,IPPG)的非接触式心率和血氧饱和度检测方法因为其方便快捷且受约束较少等优点开始逐步成为研究热点.主要工作如下:首先,介绍了非接触式检测方法的背景和研究意义;其次,从目标区域检测和感兴趣区域(region of interest,ROI)选取两个方面总结并点明其研究现状以及未来改进方向;再次,从传统方法、信号处理结合深度学习方法以及端到端方法3个方面对心率和血氧饱和度检测方法进行了总结,并梳理了深度学习方法所使用的数据集以及在各个数据集中所展现的检测效果;最后,指出该领域所存在的亟待解决的问题以及未来的研究方向.
文摘基于光电容积脉搏波描记法(Photo Plethysmo Graphy,PPG)的柔性传感器可进行心率(Heart Rate,HR)和血压(Blood Pressure,BP)检测,但是对其检测结果的标定报道甚少.据此,本文提出一种基于模拟血液循环的反射式PPG心率检测和血压标定系统.以蠕动泵来产生脉动流,通过调节其转速的大小来控制模拟血液输送的频率和压力,从而引起弹性乳胶管内模拟血液体积的变化,而改变反射光的信号周期与强度,贴近于人体脉搏测量过程的实际场景.该系统心率检测误差均值为0.27778,95%一致性界限为(-2.59562,3.15117),所测收缩压(Systolic Blood Pressure,SBP)和舒张压(Diastolic Blood Pressure,DBP)的拟合优度分别为0.97185和0.98111.经标定后的柔性PPG传感器对4名志愿者检测的SBP和DBP的平均偏差(Mean Deviation,MD)±标准差(Standard Deviation,SD)均值分别为(1.21±2.16)mmHg和(0.76±2.02)mmHg,均符合且远小于美国医疗仪器促进协会(Association for the Advancement of Medical Instrumentation,AAMI)所制定的衡量血压计精度的标准指标(5±8)mmHg.结果表明,该系统能够准确高效地标定柔性PPG传感器,为实现便携式可穿戴设备的精准血压检测提供标定基础.
