Blinking During and After Lying

Leal, Sharon; Vrij, Aldert

doi:10.1007/s10919-008-0051-0

Cited by 82 publications

(66 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The exact reason for this difference is unclear. Although the existing research has shown that increased blinking in adults while lying is reflective of experiencing an increase in cognitive load (Leal & Vrij, 2008), it is unclear why children might experience a greater cognitive load while telling a prosocial lie than while telling an antisocial lie. This issue needs to be addressed specifically in future studies.…”

Section: Discussionmentioning

confidence: 99%

Automated decoding of facial expressions reveals marked differences in children when telling antisocial versus prosocial lies

Zanette

Gao

Brunet

et al. 2016

Journal of Experimental Child Psychology

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Automated decoding of facial expressions reveals marked differences in children when telling antisocial versus prosocial lies

Zanette

Gao

Brunet

et al. 2016

Journal of Experimental Child Psychology

View full text Add to dashboard Cite

“…Such inhibition then is challenged by fatigue, leading to higher blink rates, and sleepiness, associated with longer blink durations. Nonetheless, several other factors, such as emotional excitement, verbalization, and, possibly, mental workload, may be indexed by the endogenous eyeblink (Stern et al, 1984), and recent studies have shown a growing and variegated employment of the eyeblink measure with different event detection settings (Chermahini & Hommel, 2010;Leal & Vrij, 2008;Smilek et al, 2010). Whatever the research field, a widely shared definition of eyeblink detection methods and parametrs for VEOG and eye-tracking studies could be a first step for outlining a scenario where it is possible to make comparisons across studies and across research areas for such a complex variable.…”

Section: Discussionmentioning

confidence: 99%

A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols

et al. 2011

View full text Add to dashboard Cite

Event detection is the conversion of raw eyetracking data into events-such as fixations, saccades, glissades, blinks, and so forth-that are relevant for researchers. In eye-tracking studies, event detection algorithms can have a serious impact on higher level analyses, although most studies do not accurately report their settings. We developed a data-driven eyeblink detection algorithm (Identification-Artifact Correction [I-AC]) for 50-Hz eye-tracking protocols. I-AC works by first correcting blink-related artifacts within pupil diameter values and then estimating blink onset and offset. Artifact correction is achieved with data-driven thresholds, and more reliable pupil data are output. Blink parameters are defined according to previous studies on blink-related visual suppression. Blink detection performance was tested with experimental data by visually checking the actual correspondence between I-AC output and participants' eye images, recorded by the eyetracker simultaneously with gaze data. Results showed a 97% correct detection percentage.

show abstract

“…Mann et al [57] have stated that the suspects will blink less frequently when telling lies in high-stakes situations. Leal and Vrij [99,100] have found that the blinking pattern of liars and truth-tellers differ: liars show a decreased number of eye blinks when they are lying, followed by an increase. In [52], ten Brinke and Porter have also reported a higher blink rate observed in deceptive suspects.…”

Section: Psychological Theoriesmentioning

confidence: 99%

Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception

Lin

Levine

2016

Computer Vision and Image Understanding

View full text Add to dashboard Cite

During a forensic interview, high-stakes deception is very prevalent notwithstanding the heavy consequences that may result. Studies have shown that most untrained people cannot perform well in discerning liars and truth-tellers.Thus it has become common to adopt various technical aids to compensate for this poor judgment. Examples are polygraphs, functional Magnetic Resonance Imaging (fMRI) and linguistic analysis. However, the deception indicators used in these cases are not reliable.In the popular TV program Lie to Me, micro-expressions have been used for detecting deceit during the investigation of some criminal cases. A microexpression is considered to be a rapid and involuntary facial expression which could reveal the concealed emotion. Additionally, some psychological studies have stated that certain facial actions are more difficult to inhibit if the associated facial expressions are genuine. Similarly, these facial expressions are equally difficult to fake. This has cast light on the possibility that deception could be detected by analyzing these facial actions. However, to the best knowledge of the author, there is no computer vision research that has attempted to discriminate high-stakes deception from truth using facial expressions. Therefore, this thesis aims to test the validity of facial clues to deception detection in high-stakes situations using computer vision approaches.We note that only a limited number of the existing databases have been collected specifically for deception detection studies and none of them were obtained from real-world situations. In this thesis we present a video database of actual highstakes situations, which we have created using YouTube.We have adopted 2D appearance-based methods as the methodology to characterize the 3D facial features. Instead of building a 3D head model as is the current trend, we have extracted invariant 2D features that are related to the 3D characteristic.iii In order to discern deception and honesty, we have identified the following deceptive cues from nine separate facial regions through dynamic facial analysis: eye blink, eyebrow motion, wrinkle occurrence and mouth motion. Then these cues were integrated to form a facial behavior pattern vector. A Random Forest was trained using the collected database and applied to classify the facial patterns into deceptive and truthful categories.Despite the many uncontrolled factors (illumination, head pose and facial occlusion) in the videos in our database, we have achieved an accuracy of 76.92% when discriminating liars from truth-tellers using both micro-expressions and

show abstract

Blinking During and After Lying

Cited by 82 publications

References 41 publications

Automated decoding of facial expressions reveals marked differences in children when telling antisocial versus prosocial lies

Automated decoding of facial expressions reveals marked differences in children when telling antisocial versus prosocial lies

A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols

Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception

Contact Info

Product

Resources

About