Perinasal indicators of deceptive behavior

Dcosta, Malcolm; Shastri, Dvijesh; Vilalta, Ricardo; Burgoon, Judee K.; Pavlidis, Ioannis

doi:10.1109/fg.2015.7163080

Cited by 17 publications

(13 citation statements)

References 23 publications

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“…These difficulties in administering the polygraph has also been commented on by Pavlidis et al (2002), motivating them to use a thermal imaging technique to detect lies remotely. In fact, developments in facial thermophysiology have shown facial perspiration (an indicator of stress) as obtained through thermal images to be successful in detecting lies (Dcosta et al, 2015). Recent research has also led to development of a novel contactless technology called, Transdermal Optical Imaging, to assess and monitor psychophysiological changes.…”

Section: Discussionmentioning

confidence: 99%

Using Polygraph to Detect Passengers Carrying Illegal Items

Huang

et al. 2019

Front. Psychol.

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The present study examined the effectiveness of a Modified-Comparison Questions Technique, used in conjunction with the polygraph, to differentiate between common travelers, drug traffickers, and terrorists at transportation hubs. Two experiments were conducted using a mock crime paradigm. In Experiment 1, we randomly assigned 78 participants to either a drug condition, where they packed and lied about illicit drugs in their luggage, or a control condition, where they did not pack or lie about any illegal items. In Experiment 2, we randomly assigned 164 participants to one of the two conditions in Experiment 1 or an additional bomb condition, where they packed and lied about a bomb in their luggage. For both experiments, we assessed participants’ RR interval, heart rate, peak-to-peak amplitude of Galvanic Skin Response (GSR) and all three combined, using Discriminant Analyses to determine the classification accuracy of participants in each condition. In both experiments, we found decelerated heart rates and increased peak-to-peak amplitude of GSR in guilty participants when lying in response to questions regarding their crime. We also found accurate classifications of participants, in both Experiment 1 (drug vs. control: 84.2% vs. 82.5%) and Experiment 2 (drug vs. control: 82:1% vs. 95.1%; bomb vs. control: 93.2% vs. 95.1%; drug vs. bomb: 92.3% vs. 90.9%), above chance level. These findings indicate that Modified-CQT, combined with a polygraph test, is a viable method for investigating suspects of drug trafficking and terrorism at transportation hubs such as train stations and airports.

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Section: Discussionmentioning

confidence: 99%

Using Polygraph to Detect Passengers Carrying Illegal Items

Huang

et al. 2019

Front. Psychol.

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“…( Zhang, 2012 )]. Common examples are motion capture devices like Kinect (e.g., Yu et al, 2011 ) wearable sensors for the extraction of biofeedback data or unobtrusive techniques like thermal imaging ( Pavlidis et al, 2002 ; Dcosta et al, 2015 ). Machine learning algorithms, for example ( Bartlett et al, 2005 ) can extract information from video or audio sources and process them through advanced algorithms that can automatically code facial expressions, body movement, typical gestures, emotions, glance direction and tone of speech.…”

Section: Discussionmentioning

confidence: 99%

T-Pattern Analysis and Cognitive Load Manipulation to Detect Low-Stake Lies: An Exploratory Study

et al. 2018

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Deception has evolved to become a fundamental aspect of human interaction. Despite the prolonged efforts in many disciplines, there has been no definite finding of a univocally “deceptive” signal. This work proposes an approach to deception detection combining cognitive load manipulation and T-pattern methodology with the objective of: (a) testing the efficacy of dual task-procedure in enhancing differences between truth tellers and liars in a low-stakes situation; (b) exploring the efficacy of T-pattern methodology in discriminating truthful reports from deceitful ones in a low-stakes situation; (c) setting the experimental design and procedure for following research. We manipulated cognitive load to enhance differences between truth tellers and liars, because of the low-stakes lies involved in our experiment. We conducted an experimental study with a convenience sample of 40 students. We carried out a first analysis on the behaviors’ frequencies coded through the observation software, using SPSS (22). The aim was to describe shape and characteristics of behavior’s distributions and explore differences between groups. Datasets were then analyzed with Theme 6.0 software which detects repeated patterns (T-patterns) of coded events (non-verbal behaviors) that regularly or irregularly occur within a period of observation. A descriptive analysis on T-pattern frequencies was carried out to explore differences between groups. An in-depth analysis on more complex patterns was performed to get qualitative information on the behavior structure expressed by the participants. Results show that the dual-task procedure enhances differences observed between liars and truth tellers with T-pattern methodology; moreover, T-pattern detection reveals a higher variety and complexity of behavior in truth tellers than in liars. These findings support the combination of cognitive load manipulation and T-pattern methodology for deception detection in low-stakes situations, suggesting the testing of directional hypothesis on a larger probabilistic sample of population.

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“…Therefore, it is possible to transform raw thermal image series in cutaneous blood flow image series. This method has been applied in psychophysiology study of emotion assessment [15] and deception detection [34].…”

Section: B Thermal Image Analysismentioning

confidence: 99%

Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder

et al. 2020

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Children with Autism Spectrum Disorder are identified as a group of people who has difficulties in socio-emotional interaction. Most of them lack the proper context in producing social response through facial expression and speech. Since emotion is the key for effective social interaction, it is justifiably vital for them to comprehend the correct emotion expressions and recognitions. Emotion is a type of affective states and can be detected through physical reaction and physiological signals. In general, recognition of affective states from physical reaction such as facial expression and speech for autistic children is often unpredictable. Hence, an alternative method of identifying the affective states through physiological signals is proposed. Though considered non-invasive, most of the current recognition methods require sensors to be patched on to the skin body to measure the signals. This would most likely cause discomfort to the children and mask their "true" affective states. The study proposed the use of thermal imaging modality as a passive medium to analyze the physiological signals associated with the affective states nonobtrusively. The study hypothesized that, the impact of cutaneous temperature changes due to the pulsating blood flow in the blood vessels at the frontal face area measured from the modality could have a direct impact to the different affective states of autistic children. A structured experimental setup was designed to measure thermal imaging data generated from different affective state expressions induced using different sets of audio-video stimuli. A wavelet-based technique for pattern detection in time series was deployed to spot the changes measured from the region of interest. In the study, the affective state model for typical developing children aged between 5 and 9 years old was used as the baseline to evaluate the performance of the affective state classifier for autistic children. The results from the classifier showed the efficacy of the technique and accorded good performance of classification accuracy at 88% in identifying the affective states of autistic children. The results were momentous in distinguishing basic affective states and the information could provide a more effective response towards improving social-emotion interaction amongst the autistic children. INDEX TERMS Autism, Affective States, Facial Skin Temperatures, Thermoregulation, Thermal Images, Wavelet I. INTRODUCTION F OR decades, numbers of research have been conducted in the field of affective state recognition, using numerous modalities and exploiting various features from the signal generated by the Autonomous Nervous System (ANS). ANS together with hypothalamus, regulates the blood pressure, breathing, pulse and arousal in response to different emotional features. Seeing the big potentials of affective state

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Perinasal indicators of deceptive behavior

Cited by 17 publications

References 23 publications

Using Polygraph to Detect Passengers Carrying Illegal Items

Using Polygraph to Detect Passengers Carrying Illegal Items

T-Pattern Analysis and Cognitive Load Manipulation to Detect Low-Stake Lies: An Exploratory Study

Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder

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