Polygraph Research Articles

Lie Detection Technology of Bimodal Feature Fusion Based on Domain Adversarial Neural Networks

In the domain of lie detection, a common challenge arises from the dissimilar distributions of training and testing datasets. This causes a model mismatch, leading to a performance decline of the pretrained deep learning model. To solve this problem, we propose a lie detection technique based on a domain adversarial neural network employing a dual-mode state feature. First, a deep learning neural network was used as a feature extractor to isolate speech and facial expression features exhibited by the liars. The data distributions of the source and target domain signals must be aligned. Second, a domain-antagonistic transfer-learning mechanism is introduced to build a neural network. The objective is to facilitate feature migration from the training to the testing domain, that is, the migration of lie-related features from the source to the target domain. This method results in improved lie detection accuracy. Simulations conducted on two professional lying databases with different distributions show the superiority of the detection rate of the proposed method compared to an unimodal feature detection algorithm. The maximum improvement in detection rate was 23.3% compared to the traditional neural network-based detection method. Therefore, the proposed method can learn features unrelated to domain categories, effectively mitigating the problem posed by different distributions in the training and testing of lying data.

A Review of Automatic Lie Detection from Facial Features

A Review of Automatic Lie Detection from Facial Features

LieWaves: dataset for lie detection based on EEG signals and wavelets.

This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep learning methods. EEG signals were acquired from 27 individuals using a wearable EEG device called Emotiv Insight with 5 channels (AF3, T7, Pz, T8, AF4). Each person took part in two trials: one where they were honest and another where they were deceitful. During each experiment, participants evaluated beads they saw before the experiment and stole from them in front of a video clip. This study consisted of four stages. In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. Later, the overlapping sliding window (OSW) method was used for data augmentation. In the third stage, feature extraction was performed. To achieve this, EEG signals were analyzed by combining discrete wavelet transform (DWT) and fast Fourier transform (FFT) including statistical methods (SM). In the last stage, each obtained feature vector was classified separately using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNNLSTM hybrid algorithms. At the study's conclusion, the most accurate result, achieving a 99.88% accuracy score, was produced using the LSTM and DWT techniques. With this study, a new data set was introduced to the literature, and it was aimed to eliminate the deficiencies in this field with this data set. Evaluation results obtained from the data set have shown that this data set can be effective in this field.

Verification of using radar vital sign monitors in deception tests (2023)

This study examines the validity and accuracy of a typical radar-based vital signal monitoring system during spoken responses, a common and necessary scenario in deception tests. Traditional deception tests, such as polygraph tests, often involve testees wearing numerous electrodes, which can induce nervousness and affect their physiological measurements. Additionally, testers may be hindered by the potential Hawthorne effect caused by such varied measurements, while the movement and behavior of testees are typically restricted to ensure data validity. Based on the conclusive findings of this paper, practitioners can employ radar technology to non-invasively extract crucial vital signs during deception tests, ensuring validation. This approach enhances efficiency and affords greater flexibility to testers.

From Polygraphs to Truth Machines: Artificial Intelligence in Lie Detection

From Polygraphs to Truth Machines: Artificial Intelligence in Lie Detection

Формування людського капіталу підприємства в межах рекрутингу

The article summarizes the important elements of recruiting, which demonstrates to business entities the current state of personnel recruitment and will be practical advice for enterprises (firms) that are just thinking about modern methods of personnel selection and recruitment. The aim of the article is to generalize the experience of hiring personnel from the standpoint of recruiting (both at the level of employer enterprises and at the level of agencies, i. e. recruiting companies). The authors summarized the essence of the recruiting (recruitment) process, subject composition, significance for the enterprise, modern popular options for recruiting personnel, modern recruiting technologies, areas of business that serve recruitment, methods (tools) of recruitment. Recruiting in its general form is a set of actions to provide the enterprise (employer) with personnel with the necessary competencies – quantitative and qualitative provision of the enterprise’s workers. The subject composition of recruiting includes: re-cruiters (they represent recruiting companies, personnel agencies, funds and centers, State structures for the selection of candidates to fill vacancies); employees of the personnel department, heads of divi-sions in which there are vacancies, psychologists, polygraph examiners, handwriting experts, experts and other personnel recruitment specialists (they represent relevant divisions of the enterprise or pro-fessional structures (agencies)). The advantages and disadvantages of internal and external sources of personnel recruitment are presented. Common options for job search and personnel selection are: direct application to the employer, publication of vacancies, personal recommendations, internal personnel reserve, niche resources for mass recruitment. Recruiting services (external recruitment) are used by various areas of business: pharmaceutical, IT, finance, banking, etc. If we distinguish between the announcement and closing of vacancies by the State-owned recruiting structures (State employment service bodies) and private recruiting companies, then the State recruiting structures mainly serve or-ders (vacancies) of the State-owned enterprises, organizations, and private companies – orders (vacan-cies) of private economic entities. Regardless of the type of recruiting (internal, external), the same methods (tools) are used: traditional, additional and innovative. Knowing how to apply recruiting me-thods (tools) helps HR managers to select the best job candidates. The principles of classical recruiting are considered as the most important from the point of view of overcoming unemployment and per-sonnel selection: systematization, democratization, individualization, informatization, goal orientation, delegation. The priority directions for improving recruiting activity are summarized (improving the quality of hiring, or recruitment efficiency); speeding up hiring (recruitment); increasing the flow of quality candidates for vacant positions; automating recruiting procedures; increasing employee reten-tion rates (i. e., reducing turnover and increasing employee commitment); ensuring diversity in hiring (recruitment of employees by ethnic indicators, gender, age, experience, etc.). An analysis of the quali-fication characteristics of managers, specialists, and workers was carried out, which made it possible to determine the main components of the required human capital from the point of view of the division of employees into professional groups: management personnel (intellectual component , motivational, cultural, social, economic, creative, educational, professional, environmental, social, informational, use of time); personnel from production activities (demographic component, health, educational compo-nent, professional (activity) component).

TrueWatch: Polygraph Examination With Smartwatches

TrueWatch: Polygraph Examination With Smartwatches

Physiological Reactions Profiling in Polygraph Testing: Insights from Fuzzy C-Means Clustering Analysis

Physiological Reactions Profiling in Polygraph Testing: Insights from Fuzzy C-Means Clustering Analysis

The Memoirist and the Lie Detector

The Memoirist and the Lie Detector

Current aspects and problematic issues of the use of a polygraph in public service in modern conditions

In the work, the author examines problematic issues related to the study of the peculiarities of the organization and legal regulation of the use of a polygraph in public service. The article deals with various aspects of the use of a polygraph in the public sphere, including legal aspects, psychological and pedagogical features, physiological and ethical issues, as well as the protection of citizens' rights. In addition, it examines the experience of using a polygraph in various conditions when the procedure for obtaining, identifying, processing, storing and reliability of data is carried out, and as a result, it becomes necessary for competent public sector specialists to constantly pay special attention to the safety of storing and using confidential information received. In order to analyze the effectiveness of the use of a polygraph, the paper describes the tasks facing specialists, considers algorithms and techniques used to assess the reliability of the information received. The following methods were used in the preparation of the article : an analysis of literature, a system approach, comparative analysis and synthesis, induction and deduction, logical thinking techniques, classification. The main conclusions of the research conducted by the author are organizational aspects aimed at respecting the rights and freedoms of citizens who, for one reason or another, will have to undergo the testing procedure on a polygraph device. A prerequisite is the fact that the legal regulation of the use of a polygraph in the civil service requires clear formulation, transparency and objectivity of procedures, timely clarification of the upcoming nuances of the polygraph examination, related possible restrictions, but at the same time compliance with guarantees for employees who are being tested. This topic, in its content and meaning, is relevant, and therefore, today, the development of legal and methodological mechanisms for the use of a polygraph in the public sphere, based on scientific developments by leading scientists and practitioners in this field of knowledge, is in demand.

Automatically detect crosswalks from satellite view images – A deep learning approach with ground truth verification

Like roadway information is to motor vehicle safety, pedestrian facility information (e.g., sidewalk presence) is crucial towards improving the safety of these vulnerable road users. Yet unlike widely accessible roadway data, pedestrian facility data is unavailable for most state agencies. Without this information, data-driven problem identification, countermeasure analysis, project evaluation, and performance management will be heavily impeded. Thus, urgent need for this data was recognized by state departments of transportation (DOTs). To address this need, we developed an automated approach to automatically detect crosswalks from satellite view images. The most advanced deep learning methodology, transfer learning with a Convolutional Neural Network (CNN) was used to handle real-world images. During the prediction process, a satellite image of a roadway pavement was analyzed by the satellite view model to predict the presence of a crosswalk. Then, the street view image of the same target was detected by the integrated street view model as a ground truth check. A total of 18,361 images from Bing Maps in satellite view and street view were used to train and test the deep learning model. As a result, the satellite view model itself achieved 98.43% accuracy using testing data from the same region. When dealing with data from another region, using the satellite view detection with ground truth checking increased the accuracy by 49%. It is obvious that by integrating the ground truth checking model into the satellite view crosswalk detection, we can obtain a more robust model which can handle highly occluded, low quality satellite images.

Eye-movement Technique for Lie Detection: An Empirical Study Based on Simulated Experiments

Eye-movement Technique for Lie Detection: An Empirical Study Based on Simulated Experiments

Review on Polygraph Testing in Clinical Forensic Psychology

Polygraph tests are widely employed as forensic tests in investigations and in legal proceedings. The aim of this study is to demonstrate how physiology and psychological techniques operate in lie detection or Polygraph provides a contribution to scientific methods of criminal investigation. Lie detector tests use a polygraph machine that consists of different parts to measure the changes in the physiological aspects (usually respiration, electro dermal activity, relative blood pressure, and often peripheral vasomotor activity) of a person while answering questions or reacting to statements. Polygraph testing is still used in certain contexts, its validity and reliability are matters of ongoing debate within the scientific community. Many experts argue that it should not be the sole basis for making high-stakes decisions. Polygraph tests are used frequently in many fields, such as law enforcement, national security, and private investigations etc. The constitutional validity of Polygraph Test is still unanswered. Thus, government should enact new legislation regarding application of lie detectors in criminal justice. So that, our legal system take better advantage of rapid advances in scientific tools which work as judicial aids.

Artificial Intelligence based Approach to Analyze the Lie Detection using Skin and Facial Expression

Finding a secret fact that is known to one person but kept from others is the aim of lie detection. When it comes to security and legal concerns, it is crucial to be able to analyze human deceptive behavior. Based on the idea that lying causes specific emotions, which in turn cause certain physiological reactions depending on a person's level of stress, psychological lie detection is based on this premise. Modern lie detectors measure things like skin conductance, respiration, and blood pressure. The intended study presents a lie detection system that uses fuzzy analysis to integrate many factors, including skin conductance, facial expressions (eye blinking), and voice frequency, which is actually influenced by a person's degree of stress. Fuzzy is an AI technique based on graphical representations and mathematical calculations of an output which is affected by input parameters. In comparison to employing a single indicator alone, the integration of skin analysis, voice level, and facial expression using fuzzy analysis may improve the accuracy of lie detection. This analysis provides a way to predict that a person is lying when all three parameters i.e., Skin conductance, voice frequency, and blinking of eye increases which effect on stress level of a body.

Polygraph Testing in Criminal Proceedings: Safe and Sound or Swimming Against the Tide?

Abstract In contrast to other Western-European countries, polygraph testing is being used in police investigations in Belgium for about twenty years now. However, until recently, its use was not legally regulated. On 1 January 2021 an amendment to the law came into force which officially regulates the use of the polygraph in the Belgian Code of Criminal Procedure. Yet, in light of findings from academic research polygraph testing in criminal proceedings is controversial. Therefore, these Belgian practices and developments could be considered to be an outlier. Against this background, this article examines the recent legal regulation on polygraph testing and its use in practice from both a legal and criminological-psychological perspective. As such, the article offers important insights for law and practice on polygraph testing and the use of comparable other techniques across Europe and beyond.

Respiratory event index underestimates severity of sleep apnea compared to apnea-hypopnea index.

Polygraphy (PG) is often used to diagnose obstructive sleep apnea (OSA). However, it does not use electroencephalography, and therefore cannot estimate sleep time or score arousals and related hypopneas. Consequently, the PG-derived respiratory event index (REI) differs from the polysomnography (PSG)-derived apnea-hypopnea index (AHI). In this study, we comprehensively analyzed the differences between AHI and REI. Conventional AHI and REI were calculated based on total sleep time (TST) and total analyzed time (TAT), respectively, from two different PSG datasets (n = 1561). Moreover, TAT-based AHI (AHITAT) and TST-based REI (REITST) were calculated. These indices were compared keeping AHI as the gold standard. The REI, AHITAT, and REITST were significantly lower than AHI (p < 0.0001, p ≤ 0.002, and p ≤ 0.01, respectively). The total classification accuracy of OSA severity based on REI was 42.1% and 72.8% for two datasets. Based on AHITAT, the accuracies were 68.4% and 85.9%, and based on REITST, they were 65.9% and 88.5% compared to AHI. AHI was most correlated with REITST (r = 0.98 and r = 0.99 for the datasets) and least with REI (r = 0.92 and r = 0.97). Compared to AHI, REI had the largest mean absolute errors (13.9 and 6.7) and REITST the lowest (5.9 and 1.9). REI had the lowest sensitivities (42.1% and 72.8%) and specificities (80.7% and 90.9%) in both datasets. Based on these present results, REI underestimates AHI. Furthermore, these results indicate that arousal-related hypopneas are an important measure for accurately classifying OSA severity.

Selected routine laboratory tests in the clinical assessment of patients with obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a chronic disease characterized by repetitive complete or partial occlusion of the upper airways during sleep with respiratory muscle effort, which leads to consecutive apneas and hypopneas. Obstruction of the upper airways during sleep leads to repetitive episodes of disrupted airflow and consequent changes in blood oxygenation, resulting in hypoxaemia and hypercapnia. Intermittent hypoxaemia induces the production of pro-inflammatory factors and promotes metabolic dysregulation and platelet aggregation. The main aim of this study was to determine differences, if any, in selected standard parameters in routine laboratory tests often used in GP practice between patients with obstructive sleep apnea, without comorbidities, and a well-defined control group with the absence of this syndrome proven in polygraphic examination. Of the 192 clinically assessed persons with suspected OSA and admitted to the Internal Medicine Department in Lublin, 85 were qualified for the study after application of exclusion criteria. Demographic and health behaviour-related data, medical history regarding sleep habits and cardiovascular disease, were collected from each patient. Apart from significantly higher MCV and MCH among the cpontrol group, no significant differences were found between patients with obstructive sleep apnea and the control group. The results can be useful for the holistic assessment of the health status of patients with newly-diagnosed OSA.

The Communication Patterns of Highly Effective Lie Detectors

ABSTRACT A prior experiment involving professional interrogators using unscripted questions produced exceptionally high (98%) deception detection accuracy. Guided by truth-default theory, videotapes of those interviews were examined and coded to better understand how the unusually high degree of accuracy might have been obtained. Eighty-four videotaped interviews of potential cheaters by five professional interrogators were used to develop a coding scheme consisting of 78 potentially relevant behaviors. Consistent with truth-default theory, the experts explored communication motives that might make deceit more likely, sought contextually relevant communication, circled back in questioning with attention to inconsistencies, and persuaded honest confessions. Observed strategies for persuading honesty included pitting the interviewee against their partner, minimizing the act of cheating, normalizing cheating as just human nature, appealing to the interviewee’s character, appealing to the integrity of the research or data, and guilt appeals. The findings are discussed in terms of practical and theoretical implications.

Intelligent Eye-Tracker-Based Methods for Detection of Deception: A Survey

Over the last few years, a large number of studies have been conducted on the monitoring of human behavior remaining beyond conscious control. One area of application for such monitoring systems is lie detection. The most popular method currently used for this purpose is polygraph examination, which has proven its usefulness in the field and in laboratories, but it is not without its drawbacks. Technological advances in data acquisition and automated analysis have ensured that contactless tools are in high demand in security fields like airport screening or pre-employment procedures. As a result, there has been a shift in interest away from traditional polygraph examinations toward the analysis of facial expressions, voice, and speech patterns, as well as eye-tracking signals to detect deceptive behavior. In this paper, we focus on the last aspect, offer a comprehensive overview of two distinct lie detection methodologies based on eye tracking, and examine the commonly used oculomotor feature analysis. Furthermore, we explore current research directions and their results within the context of their potential applications in the field of forensics. We also highlight future research prospects, suggesting the utilization of eye tracking and scan path interpretation methodologies as a potential fully functional alternative for the conventional polygraph in the future. These considerations refer to legal and ethical issues related to the use of new technology to detect lies.

Topic Audiolization: A Model for Rumor Detection Inspired by Lie Detection Technology

The psychological factors influence the frequency and intensity of the lie’s audio. In addition, the changes could be associated with each word in a segment of the liar’s output. We consider a single comment as the audio signal of a word and the entire propagation process as the audio signal of a paragraph. Thus, rumor detection based on propagation features is transformed into lie detection based on audio signals. Firstly, we propose a Topic2Audio method to transform the topic space into an audio-like signal. This method quantifies topic propagation features and maps them to the audio-like signal’s amplitude, frequency, and offset. Secondly, we retrieved audio features using Fourier transforms and Mel spectrum algorithms. Finally, we propose a rumor detection model based on topic audiolization (TARD). The model can detect ‘rumor’ signals using audio classification techniques by transforming the topic space into audio-like signals. Experiments conducted on three datasets, i.e., Weibo, Politifact, and Gossipcop, show performance improvements at +2.2%, +1.9%, and +1.3% when compared with the state-of-the-art method, respectively. Experiments show that our model can identify rumors effectively.