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Video-Based deception detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

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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Hybrid Metaheuristics with Deep Learning Enabled Automated deception detection and Classification of Facial Expressions

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Computers, Materials & Continua 2023年第6期75卷 5433-5449页

作者： Haya Alaskar Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Automatic deception recognition has received considerable atten-tion from the machine learning community due to recent research on its vast application to social media,interviews,law enforcement,and the *** analysis-based techniques for automated deception detection have received increasing *** study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection(SAPFF-DLADD)model for analyzing facial ***-tially,the input video is separated into a set of video ***,the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of *** long short-term memory(LSTM)model is exploited for deception detection and *** the final stage,the SAPFF technique is applied to optimally alter the hyperparameter values of the LSTM model,showing the novelty of the *** experimental validation of the SAPFF-DLADD model is tested using the Miami University deception detection Database(MU3D),a database comprised of two classes,namely,truth and *** extensive comparative analysis reported a better performance of the SAPFF-DLADD model compared to recent approaches,with a higher accuracy of 99%.

关键词： deception detection facial cues deep learning computer vision hyperparameter tuning

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The "honest" brain region - Decoding true answers from brain activity

The "honest" brain region - Decoding true answers from brain...

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中国神经科学学会第九届全国学术会议暨第五次会员代表大会

作者： Zi-Rui HUANG~(1,2,3),Zhi YANG~(1,*),Rui DAI~(3,4),Xu-Chu WENG~3 1 Institute of Psychology,Chinese Academy of Sciences,Beijing,100101,China 2 Graduate School of Chinese Academy of Sciences,Beijing,100049,China 3 Research Center of Human Brain and Mental Disease,Hangzhou Normal University,310015,China 4 Institute of Life Science,South China Normal University,Guangzhou,510631,China

＜正＞Men lie for various reasons,hiding their true thoughts which cognitive neuroscientists are eager to read out using neuroimaging techniques yet have to date not reached a clear-cut *** most studies have compared the brain response patterns between honest and deceptive answers,here we directly examined whether the faithful answers for polar questions can be decoded from BOLD *** found a unique region in the left middle frontal gyrus that successfully discriminated between positive and negative faithful responses independent of intentional suppression and stimuli *** propose the activity of this "honest" region may serve as an indicator of one’s faithful answers.

关键词： deception detection searchlight fMRI faithful answers

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