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
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 mil-itary.Vide...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 mil-itary.Video analysis-based techniques for automated deception detection have received increasing interest.This study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection(SAPFF-DLADD)model for analyzing facial cues.Ini-tially,the input video is separated into a set of video frames.Then,the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of features.The long short-term memory(LSTM)model is exploited for deception detection and classification.In the final stage,the SAPFF technique is applied to optimally alter the hyperparameter values of the LSTM model,showing the novelty of the work.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 deception.An extensive comparative analysis reported a better performance of the SAPFF-DLADD model compared to recent approaches,with a higher accuracy of 99%.展开更多
Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions.The human face is a rich source of data that offers trustworthy markers of deception.The deception or lie ...Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions.The human face is a rich source of data that offers trustworthy markers of deception.The deception or lie detection systems are non-intrusive,cost-effective,and mobile by identifying facial expressions.Over the last decade,numerous studies have been conducted on deception detection using several advanced techniques.Researchers have focused their attention on inventing more effective and efficient solutions for the detection of deception.So,it could be challenging to spot trends,practical approaches,gaps,and chances for contribution.However,there are still a lot of opportunities for innovative deception detection methods.Therefore,we used a variety of machine learning(ML)and deep learning(DL)approaches to experiment with this work.This research aims to do the following:(i)review and analyze the current lie detection(LD)systems;(ii)create a dataset;(iii)use several ML and DL techniques to identify lying;and(iv)create a hybrid model known as LDNet.By combining layers from Vgg16 and DeneseNet121,LDNet was developed and offered the best accuracy(99.50%)of all the models.Our developed hybrid model is a great addition that significantly advances the study of LD.The findings from this research endeavor are expected to advance our understanding of the effectiveness of ML and DL techniques in LD.Furthermore,it has significant practical applications in diverse domains such as security,law enforcement,border control,organizations,and investigation cases where accurate lie detection is paramount.展开更多
The“success”of a polygraph examination is predicated on the establishment of differential or emotional salience(a“psychological set”)with an examinee.This,according to polygraph proponents,guarantees that an exami...The“success”of a polygraph examination is predicated on the establishment of differential or emotional salience(a“psychological set”)with an examinee.This,according to polygraph proponents,guarantees that an examinee will respond appropriately during the administration of the in-test(questioning)phase of the polygraph examination.However,polygraph procedure,as prescribed by its governing body,the American Polygraph Association(APA),is a static clinical Westernised process that does not make any provision for human multiplicity(culture/ethnicity,idiosyncrasies,level of education,language proficiency,ideologies,and so forth).Identical(one size fits all)test procedures are applied across the board–a highly controversial methodology.This article,instead of rigidly focusing on validity and reliability issues per se,explores the degree to which certain intentional and unintentional human behaviour modification strategies have the potential to counterbalance claimed polygraph rectitude from a metaphysical and discursive standpoint.The article exposes concerns(potential flaws)relating to polygraph theory in the context of the“psychological set”and is intended to serve as a caveat regarding the unmitigated use thereof.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.
文摘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 mil-itary.Video analysis-based techniques for automated deception detection have received increasing interest.This study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection(SAPFF-DLADD)model for analyzing facial cues.Ini-tially,the input video is separated into a set of video frames.Then,the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of features.The long short-term memory(LSTM)model is exploited for deception detection and classification.In the final stage,the SAPFF technique is applied to optimally alter the hyperparameter values of the LSTM model,showing the novelty of the work.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 deception.An extensive comparative analysis reported a better performance of the SAPFF-DLADD model compared to recent approaches,with a higher accuracy of 99%.
基金funded by the Ministry of Higher Education(MOHE),Malaysia under the Fundamental Research Grant Project(FRGS/1/2021/SS0/TAYLOR/02/6)。
文摘Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions.The human face is a rich source of data that offers trustworthy markers of deception.The deception or lie detection systems are non-intrusive,cost-effective,and mobile by identifying facial expressions.Over the last decade,numerous studies have been conducted on deception detection using several advanced techniques.Researchers have focused their attention on inventing more effective and efficient solutions for the detection of deception.So,it could be challenging to spot trends,practical approaches,gaps,and chances for contribution.However,there are still a lot of opportunities for innovative deception detection methods.Therefore,we used a variety of machine learning(ML)and deep learning(DL)approaches to experiment with this work.This research aims to do the following:(i)review and analyze the current lie detection(LD)systems;(ii)create a dataset;(iii)use several ML and DL techniques to identify lying;and(iv)create a hybrid model known as LDNet.By combining layers from Vgg16 and DeneseNet121,LDNet was developed and offered the best accuracy(99.50%)of all the models.Our developed hybrid model is a great addition that significantly advances the study of LD.The findings from this research endeavor are expected to advance our understanding of the effectiveness of ML and DL techniques in LD.Furthermore,it has significant practical applications in diverse domains such as security,law enforcement,border control,organizations,and investigation cases where accurate lie detection is paramount.
文摘The“success”of a polygraph examination is predicated on the establishment of differential or emotional salience(a“psychological set”)with an examinee.This,according to polygraph proponents,guarantees that an examinee will respond appropriately during the administration of the in-test(questioning)phase of the polygraph examination.However,polygraph procedure,as prescribed by its governing body,the American Polygraph Association(APA),is a static clinical Westernised process that does not make any provision for human multiplicity(culture/ethnicity,idiosyncrasies,level of education,language proficiency,ideologies,and so forth).Identical(one size fits all)test procedures are applied across the board–a highly controversial methodology.This article,instead of rigidly focusing on validity and reliability issues per se,explores the degree to which certain intentional and unintentional human behaviour modification strategies have the potential to counterbalance claimed polygraph rectitude from a metaphysical and discursive standpoint.The article exposes concerns(potential flaws)relating to polygraph theory in the context of the“psychological set”and is intended to serve as a caveat regarding the unmitigated use thereof.
