• Korean Journal of Biological Psychiatry
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• 제22권3호
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• pp.109-112
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• 2015

Since the early 2000s, there has been a continued interest in lie detection using functional magnetic resonance imaging (fMRI) in neuroscience and forensic sciences, as well as in newly emerging fields including neuroethics and neurolaw. Related fMRI studies have revealed converging evidence that brain regions including the prefrontal cortex, anterior cingulate cortex, parietal cortex, and anterior insula are associated with deceptive behavior. However, fMRI-based lie detection has thus far not been generally accepted as evidence in court, as methodological shortcomings, generalizability issues, and ethical and legal concerns are yet to be resolved. In the present review, we aim to illustrate these achievements and limitations of fMRI-based lie detection.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.165-170
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• 2022

Lying is nuisance to all, and all liars knows it is nuisance but still keep on lying. Sometime people are in confusion how to escape from or how to detect the liar when they lie. In this research we are aiming to establish a dynamic platform to identify liar by using video analysis especially by calculating the ratio of changes in their appearance when they lie. The platform will be developed using a machine learning algorithm along with the dynamic classifier to classify the liar. For the experimental analysis the dataset to be processed in two dimensions (people lying and people tell truth). Both parameter of facial appearance will be stored for future identification. Similarly, there will be standard parameter to be built for true speaker and liar. We hope this standard parameter will be able to diagnosed a liar without a pre-captured data.

• Korean Journal of Forensic Psychology
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• 제11권2호
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• pp.155-171
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• 2020

The purpose of this study is to examine the effects of emotional arousal and cognitive load on pupil diameter during a lie detection interview. The guilty group (n = 30) committed a mock crime (i.e., stealing cash) and the innocent group (n = 30) performed a mission (i.e., sending a message) in the research assistant's office. After that, their pupil size was measured using a wearable eye-tracker during the interview. The interview questions were classified with the three cognitive load, three emotional arousal, and three neutral questions. The results indicate that the main effects of group and time were not significant, but the interaction between group and time was significant. It means that when answering cognitive load questions, the guilty group showed larger increase in pupil diameter than the innocent group. The present study suggests that inducing cognitive load is more effective than inducing emotional arousal during an interview when using pupil diameter as an index of deception, and it is expected to improve the accuracy of lie detection.

• Convergence Security Journal
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• 제15권3_1호
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• pp.41-51
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• 2015

Human have more complicate and skilled ability for lying even cheat ourself. It is not easy to cheat unconscious things like sweat, eyes, or voice, but if some one cheat own self, he can cheat every of that. Lie is one of the way to spread our gene and our instinct make a lie. Every living organism even bacteria or virus use similar trick to survive. In human body, there are more complicate and profound mechanism for lying like breathe, sweat, eyes, face or voice. We can control some of that and make a fake, but it can't be perfect. Human also called 'Homo Fallax' cause we have a language and skill to lie with it. In present, we can detect lie with polygraph, but it has few weakness. So we try to use Vibraimage technology for resolve it. In this paper, we describe how to use Vibraimage for lie detection and the research history.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.263-267
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• 2005

Building classifiers for financial real-world classification problems is often plagued by severely overlapping and highly skewed class distribution. New performance measures such as receiver operating characteristic (ROC) curve and area under ROC curve (AUC) have been recently introduced in evaluating and building classifiers for those kind of problems. They are, however, in-effective to evaluation of classifier's discrimination performance in a particular class of the classification problems that interests lie in only a local operating range of the classifier, In this paper, a new method is proposed that enables us to directly improve classifier's discrimination performance at a desired local operating range by defining and optimizing a partial area under ROC curve or domain-specific curve, which is difficult to achieve with conventional classification accuracy based learning methods. The effectiveness of the proposed approach is demonstrated in terms of fraud detection capability in a real-world fraud detection problem compared with the MSE-based approach.

• Science of Emotion and Sensibility
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• 제20권3호
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• pp.109-118
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• 2017

This study examined the effects of button response to probe and irrelevant stimuli on P300 amplitude and lie detection rate in P300-based concealed information test. Participants underwent the P300-based concealed information test (P300 CIT) in two conditions. In one button condition participants were instructed to press the left mouse button only when the target was present. In two button condition, they were asked to press the left mouse button for target and right button for non-target. The results showed that the response time to target stimulus was not significantly different between the two conditions, and the response time to the probe stimulus was significantly longer than the irrelevant stimulus. The P300 amplitudes for the probe and irrelevant stimulus were all smaller in one button condition compared to two button condition. However, the P300 amplitude difference between the probe stimulus and the irrelevant stimulus did not show a significant difference in the two experimental conditions, and the lie detection rate did not differ significantly between the two conditions. Based on these findings, the effect of button response on P300 CIT with a short inter-stimulus interval was discussed.

• Science of Emotion and Sensibility
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• 제21권3호
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• pp.61-72
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• 2018

Items related to a crime that are known only to criminals and investigators can be used in the concealed information test (CIT) to assess whether the suspect is guilty of the offense. However, in many cases wherein the suspect is exposed to information about the crime, the CIT cannot be used. Although the perpetrator's memories about the details of the crime are episodic, the memories of a suspect who has inadvertently discovered the details of the crime are more likely to be semantic. The retrieval of episodic memories is associated with theta wave activity, whereas that of semantic memories is associated with alpha wave activity. Therefore, these aspects of memory retrieval can be useful in identifying the perpetrator of the crime. In this study, P300-based CITs were conducted in a guilty participant in a mock crime and an innocent participant who has been given information about the simulated offense. The results demonstrate that the difference in P300 amplitudes between the probe and the irrelevant stimulus did not differ between the guilty and innocent conditions. As expected, the lower theta band power (4-6 Hz) was higher in the probe than in the irrelevant stimulus in the guilty condition, but there was no difference in the innocent condition. Conversely, the upper alpha band power (8-10 Hz) was lower in the probe than in the irrelevant stimulus in the innocent condition, but there was no difference in the guilty condition. The possibility of using theta and alpha band powers in lie detection is discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2804-2823
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• 2013

Wireless sensor network (WSN) is a promising technology for monitoring physical phenomena at fine-grained spatial and temporal resolution. However, the typical approach of sending each sensed measurement out of the network for detailed spatial analysis of transient physical phenomena may not be an efficient or scalable solution. This paper focuses on in-network physical phenomena detection schemes, particularly the distributed computation of the boundary of physical phenomena (i.e. event), to support energy efficient spatial analysis in wireless sensor networks. In-network processing approach reduces the amount of network traffic and thus achieves network scalability and lifetime longevity. This study investigates the recent advances in distributed event detection based on in-network processing and includes a concise comparison of various existing schemes. These boundary detection schemes identify not only those sensor nodes that lie on the boundary of the physical phenomena but also the interior nodes. This constitutes an event geometry which is a basic building block of many spatial queries. In this paper, we introduce the challenges and opportunities for research in the field of in-network distributed event geometry boundary detection as well as illustrate the current status of research in this field. We also present new areas where the event geometry boundary detection can be of significant importance.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제44권4호
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.34-39
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• 2002

Road information is very important for topographic mapping, transportation application, urban planning and other related application fields. Therefore, automatic detection of road networks from spatial imagery, such as aerial photos and satellite imagery can play a central role in road information acquisition. In this paper, we use least squares correlation matching alone for road center tracking and show that it works. We assumed that (bright) road centerlines would be visible in the image. We further assumed that within a same road segment, there would be only small differences in brightness values. This algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation at the target window. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. A 1m resolution IKONOS images over Seoul and Daejeon were used for tests. The results showed that this algorithm could extract road centerlines in any orientation and help in fast and exact he ad-up digitization/vectorization of cartographic images.