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

作　　者：Yanfeng Li Jincheng Bian Yiqun Gao Rencheng Song 

机构地区：[1]Department of Biomedical Engineering,Hefei University of Technology,Hefei 230009,China

出　　处：《Journal of Beijing Institute of Technology》2024年第3期175-185,共11页北京理工大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.62271186);Anhui Key Project of Research and Development Plan(No.202104d07020005)。

摘　　要：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.

关 键 词：deception detection apparent visual features remote photoplethysmography non-contact heart rate feature selection 

分 类 号：D918.2[政治法律—法学] TP391.41[自动化与计算机技术—计算机应用技术]