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Added more secondary literature sources for Pavlidis' deception detection research.
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In collaboration with Mark Frank, Pavlidis carried out naturalistic deception studies with unwired participants who chose freely to deceive interviewers, if they thought it would help an issue dear to them, but knowing if their deception failed, their issue would suffer. This was an unprogrammed, high stakes behavior, producing real-life `fight or flight’ responses. Pavlidis and colleagues showed that pointed questions startle deceptive subjects, increasing their periorbital blood flow.<ref name="Nature2002">{{cite journal |last1=Pavlidis|first1=Ioannis|last2=Eberhardt |first2=Norman|last3=Levine |first3=James |title=Seeing through the face of deception |journal=Nature |date=January 3, 2002 |volume=415 |pages=35 |doi=10.1038/415035a}}</ref><ref name="IJCV2007">{{cite journal |last1=Tsiamyrtzis |first1=Panagiotis |last2=Dowdall |first2=Jonathan |last3=Shastri |first3=Dvijesh |last4=Pavlidis |first4=Ioannis |last5=Frank |first5=Mark |last6=Ekman |first6=Paul |title=Imaging facial physiology for the detection of deceit |journal=International Journal of Computer Vision |date=February 2007 |volume=71 |issue=2 |pages=197-214 |doi=10.1007/s11263-006-6106-y}}</ref> This finding linked errant human communication with unnecessary ocular activation – a bio-evolutionary remnant of `fight or flight’ responses in physical danger.
In collaboration with Mark Frank, Pavlidis carried out naturalistic deception studies with unwired participants who chose freely to deceive interviewers, if they thought it would help an issue dear to them, but knowing if their deception failed, their issue would suffer. This was an unprogrammed, high stakes behavior, producing real-life `fight or flight’ responses. Pavlidis and colleagues showed that pointed questions startle deceptive subjects, increasing their periorbital blood flow.<ref name="Nature2002">{{cite journal |last1=Pavlidis|first1=Ioannis|last2=Eberhardt |first2=Norman|last3=Levine |first3=James |title=Seeing through the face of deception |journal=Nature |date=January 3, 2002 |volume=415 |pages=35 |doi=10.1038/415035a}}</ref><ref name="IJCV2007">{{cite journal |last1=Tsiamyrtzis |first1=Panagiotis |last2=Dowdall |first2=Jonathan |last3=Shastri |first3=Dvijesh |last4=Pavlidis |first4=Ioannis |last5=Frank |first5=Mark |last6=Ekman |first6=Paul |title=Imaging facial physiology for the detection of deceit |journal=International Journal of Computer Vision |date=February 2007 |volume=71 |issue=2 |pages=197-214 |doi=10.1007/s11263-006-6106-y}}</ref> This finding linked errant human communication with unnecessary ocular activation – a bio-evolutionary remnant of `fight or flight’ responses in physical danger.


Pavlidis’ `fight or flight’ analysis of deception was one of the first breakthroughs attributable to the fledging affective computing field. His ''Nature'' article on the topic<ref name="Nature2002" /> put the new affective computing methods on the map and deeply influenced research on deceptive behaviors. In the aftermath of his publications, the deception detection literature moved from obtrusive sensors and heuristics to unobtrusive sensors and computational algorithms.<ref name="Rajoub2014">{{cite journal |last1=Ragoub |first1=Bashar |last2=Zwiggelaar |first2=Reyer |title=Thermal facial analysis for deception detection |journal=IEEE Transactions on Information Forensics and Securit |date=June 2014 |volume=9 |issue=6 |pages=1015-1023 |doi=10.1109/TIFS.2014.2317309}}</ref><ref name="Abouelenien2014">{{cite journal |last1=Abouelenien |first1=Mohamed |last2=Pérez-Rosas |first2=Verónica |last3=Mihalcea |first3=Rada |last4=Burzo |first4=Mihai |title=Deception detection using a multimodal approach |journal=Proceedings of the 16th International Conference on Multimodal Interaction |date=November 2014 |pages=58-65 |doi=10.1145/2663204.2663229}}</ref> These futuristic methods of deception detection also entered popular culture. Pavlidis' system featured in episode 18 of the Discovery Channel’s `Weird Connections' series,<ref name="DiscoveryWeirdConnections">{{cite web |title=Episode 18: Lying |url=https://www.discoveryuk.com/series/weird-connections/ |website=Weird Connections |publisher=Discovery |access-date=24 April 2023}}</ref> and inspired the interview technologies shown in the drama series `Agency' of CBS.<ref name="CBSAgency">{{cite web |title=The Agency |url=https://www.imdb.com/title/tt0285332/ |publisher=CBS |access-date=24 April 2023}}</ref> Ioannis Pavlidis' initial collaboration with James Levine from Mayo Clinic and his subsequent research journey on unlocking deceptive behaviors have been chronicled by journalist Evan Ratliff and colleagues in their book SAFE.<ref name="SAFE2015">{{cite book |last1=Martha |first1=Baer |last2=Katrina |first2=Heron |last3=Oliver |first3=Morton |last4=Evan |first4=Ratliff |title=SAFE: The Race to Protect Ourselves in a Newly Dangerous World |date=January 18, 2005 |publisher=HarperCollins |isbn=0-06-057715-0}}</ref>
Pavlidis’ `fight or flight’ analysis of deception was one of the first breakthroughs attributable to the fledging affective computing field. His ''Nature'' article on the topic<ref name="Nature2002" /> put the new affective computing methods on the map and deeply influenced research on deceptive behaviors. In the aftermath of his publications, the deception detection literature moved from obtrusive sensors and heuristics to unobtrusive sensors and computational algorithms.<ref name="Park2013">{{cite journal |last1=Park |first1=Kevin |last2=Suk |first2=Hey |last3=Hwang |first3=Heungsun |last4=Lee |first4=Jang-Han |title=A functional analysis of deception detection of a mock crime using infrared thermal imaging and the Concealed Information Test |journal=Frontiers in Human Neuroscience |date=March 2013 |volume=7 |doi=10.3389/fnhum.2013.00070}}</ref><ref name="Rajoub2014">{{cite journal |last1=Ragoub |first1=Bashar |last2=Zwiggelaar |first2=Reyer |title=Thermal facial analysis for deception detection |journal=IEEE Transactions on Information Forensics and Securit |date=June 2014 |volume=9 |issue=6 |pages=1015-1023 |doi=10.1109/TIFS.2014.2317309}}</ref><ref name="Abouelenien2014">{{cite journal |last1=Abouelenien |first1=Mohamed |last2=Pérez-Rosas |first2=Verónica |last3=Mihalcea |first3=Rada |last4=Burzo |first4=Mihai |title=Deception detection using a multimodal approach |journal=Proceedings of the 16th International Conference on Multimodal Interaction |date=November 2014 |pages=58-65 |doi=10.1145/2663204.2663229}}</ref><ref name="Derakhshan2020">{{cite journal |last1=Derakhshan |first1=Amin |last2=Mikaeili |first2=Mohammad |last3=Gedeon |first3=Tom |last4=Nasrabadi |first4=Ali Motie |title=Identifying the optimal features in multimodal deception detection |journal=Multimodal Technologies and Interaction |date=June 2020 |volume=4 |issue=2 |doi=10.3390/mti4020025}}</ref> These futuristic methods of deception detection also entered popular culture. Pavlidis' system featured in episode 18 of the Discovery Channel’s `Weird Connections' series,<ref name="DiscoveryWeirdConnections">{{cite web |title=Episode 18: Lying |url=https://www.discoveryuk.com/series/weird-connections/ |website=Weird Connections |publisher=Discovery |access-date=24 April 2023}}</ref> and inspired the interview technologies shown in the drama series `Agency' of CBS.<ref name="CBSAgency">{{cite web |title=The Agency |url=https://www.imdb.com/title/tt0285332/ |publisher=CBS |access-date=24 April 2023}}</ref> Ioannis Pavlidis' initial collaboration with James Levine from Mayo Clinic and his subsequent research journey on unlocking deceptive behaviors have been chronicled by journalist Evan Ratliff and colleagues in their book SAFE.<ref name="SAFE2015">{{cite book |last1=Martha |first1=Baer |last2=Katrina |first2=Heron |last3=Oliver |first3=Morton |last4=Evan |first4=Ratliff |title=SAFE: The Race to Protect Ourselves in a Newly Dangerous World |date=January 18, 2005 |publisher=HarperCollins |isbn=0-06-057715-0}}</ref>


'''Studies on Driving Distractions and Micro-stressors.'''
'''Studies on Driving Distractions and Micro-stressors.'''

Revision as of 18:03, 26 April 2023

Ioannis Thomas Pavlidis
BornSeptember 12, 1963
Alma materUniversity of Minnesota

Imperial College

Democritus University
Scientific career
InstitutionsUniversity of Houston
Doctoral advisorNikolaos N. Papanikolopoulos
Websitehttps://www.cpl.uh.edu

Ioannis Thomas Pavlidis (born September 12, 1963) is a Greek American scholar.[1] He is the distinguished Eckhard-Pfeiffer Professor of Computer Science at the University of Houston, founder, and director of the Affective and Data Computing Laboratory, formerly known as the Computational Physiology Lab (CPL).

Pavlidis was the first who conceived and developed contact-free methods to measure heart function, breathing function, and electrodermal activity (EDA). The realization of contactless physiological measurement methods for monitoring emotions and wellness[2] were key successes of the fledging affective computing and health informatics fields in the 2000s. Pavlidis is also credited with the design of influential naturalistic studies in deceptive behaviors and driving distractions, which he conducted using the technical methods he developed earlier.[3] It is mainly through these studies and their results that Ioannis Pavlidis is known to the broader public.[4]

Contactless Physiological Measurements

In research between 2000 and 2012, Pavlidis transformed physiological measurements, impacting affective computing and personal health informatics.  Affective computing has been relying on heart function, breathing function, and electrodermal activity (EDA) to estimate subjects’ emotional arousal levels. Heart and breathing functions also happen to be vital signs used in health care.  Conventionally, heart and breathing functions were measured with tethered body sensors, while EDA was measured with galvanic skin response (GSR) sensors attached to the palm. Such obtrusive measurement methods were rendering continuous physiological monitoring impractical and were undercutting the aim of affective computing to understand human emotions. For instance, EDA palm sensing precluded affective monitoring when the subjects’ hands were at work, like in driving.  Pavlidis was the first to conceive, develop, and validate contactless physiological measurement methods. He achieved this by replacing sensors with thermal imaging trackers, and electronic devices with thermophysiological models. His models were estimating heart (2001-2008), breath (2004-2010), and EDA signals (2009-2012) by operating on imagery of facial vasculature, the nostrils, and the perinasal region, respectively.  The latter was also a significant discovery, as the existence of facial EDA responses was unknown up to that time.

Pavlidis first articulated his innovative ideas for contactless, continuous, and automated physiological measurements in the visionary paper `Continuous physiological monitoring', which appeared in the 2003 IEEE Engineering in Medicine and Biology Society (EMBS) conference proceedings.[5] In 2004, Pavlidis and his colleagues reported the first ever imaging methods for contactless measurement of blood flow and breath in IEEE CVPR [6] and IEEE EMBS, [7] respectively. In the 2005 CVPR, Pavlidis and his colleagues followed up with the first ever imaging method for contactless measurement of pulsation. [8] The evolution of the contactless pulse and breath measurement methods culminated with Pavlidis’ seminal IEEE TBME publications. [9][10]

In 2009, Pavlidis reported progress on his third and final objective – a thermal imaging method to measure EDA responses on the face. Pavlidis placed emphasis on modeling and meticulously validating the phenomenon itself in his first paper on the matter.[11] After he documented the existence of facial EDA,[11] Pavlidis and his group published in 2012 a follow-up paper in the IEEE Transactions on Affective Computing,[12] describing two elegant computational methods to remotely measure facial EDA - one method was based on image morphology while the other was based on spatial isotropic wavelets.

Influential Naturalistic Behavioral Studies

Ioannis Pavlidis demonstrated that innovative affective computing methods must be employed within new research designs to deliver on their promise of understanding the human state. His studies approached humans as complex systems, unraveling the intricacies of the `fight or flight’ syndrome in deceptive behaviors and driving distractions.

Studies on Deceptive Behaviors. In collaboration with Mark Frank, Pavlidis carried out naturalistic deception studies with unwired participants who chose freely to deceive interviewers, if they thought it would help an issue dear to them, but knowing if their deception failed, their issue would suffer. This was an unprogrammed, high stakes behavior, producing real-life `fight or flight’ responses. Pavlidis and colleagues showed that pointed questions startle deceptive subjects, increasing their periorbital blood flow.[13][14] This finding linked errant human communication with unnecessary ocular activation – a bio-evolutionary remnant of `fight or flight’ responses in physical danger.

Pavlidis’ `fight or flight’ analysis of deception was one of the first breakthroughs attributable to the fledging affective computing field. His Nature article on the topic[13] put the new affective computing methods on the map and deeply influenced research on deceptive behaviors. In the aftermath of his publications, the deception detection literature moved from obtrusive sensors and heuristics to unobtrusive sensors and computational algorithms.[15][16][17][18] These futuristic methods of deception detection also entered popular culture. Pavlidis' system featured in episode 18 of the Discovery Channel’s `Weird Connections' series,[19] and inspired the interview technologies shown in the drama series `Agency' of CBS.[20] Ioannis Pavlidis' initial collaboration with James Levine from Mayo Clinic and his subsequent research journey on unlocking deceptive behaviors have been chronicled by journalist Evan Ratliff and colleagues in their book SAFE.[21]

Studies on Driving Distractions and Micro-stressors. Pavlidis showed that both absent-minded driving and texting while driving generate `fight or flight’ related hand-tremors. The difference, however, is that with eyes constantly on the road, the absent-minded drivers’ anterior cingulate cortex can still manage driving subconsciously, by `hijacking’ the hand-eye feedback loop and counterbalancing tremors; this is not possible for texting drivers, with eyes intermittently on the road.[22] This finding established the unmitigated danger texting poses to driving safety and had broad impact.[23]

In more recent work, Pavlidis demonstrated that a portion of the driving population exhibits significant stress responses even in trivial acceleration events, such as stop-and-go traffic - a phenomenon termed `accelarousal'.[24] Accelarousal is likely associated with genetic predisposition and is now considered a prime example of daily micro-stressor, which many believe should factor into the design of self-driving cars.[25]

References

  1. ^ Pavlidis, Ioannis. "Google Scholar Profile". Retrieved 25 April 2023.
  2. ^ Cruikshank, Dana. "Computer science provides a more sound way to test for sleep apnea". NSF. Retrieved 25 April 2023.
  3. ^ Blitzer, Wolf. "Brian Todd Reporting on New Interrogation Technologies". CNN. Situation Room. Retrieved 25 April 2023.
  4. ^ Doyle, Kathryn. "A 'sixth sense' may protect drivers, except while texting". Reuters Health. Reuters. Retrieved 25 April 2023.
  5. ^ Pavlidis, Ioannis (September 2003). "Continuous physiological monitoring". Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 1084–1087. doi:10.1109/IEMBS.2003.1279434.
  6. ^ Garbey, Marc; Merla, Arcangelo; Pavlidis, Ioannis (June 2004). "Estimation of blood flow speed and vessel location from thermal video". Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition – CVPR 2004: 356–363. doi:10.1109/CVPR.2004.1315054.
  7. ^ Murthy, Ramya; Pavlidis, Ioannis; Tsiamyrtzis, Panagiotis (September 2004). "Touchless monitoring of breathing function". Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2: 1196–1199.
  8. ^ Sun, Nanfei; Garbey, Marc; Merla, Arcangelo; Pavlidis, Ioannis (June 2005). "Imaging the cardiovascular pulse". Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition – CVPR 2005. 2: 416–421.
  9. ^ Garbey, Marc; Sun, Nanfei; Merla, Arcangelo; Pavlidis, Ioannis (August 2007). "Contact-free measurement of cardiac pulse based on the analysis of thermal imagery". IEEE Transactions on Biomedical Engineering. 54 (8): 1418–1426. doi:10.1109/TBME.2007.891930.
  10. ^ Fei, Jin; Pavlidis, Ioannis (April 2010). "Thermistor at a distance: Unobtrusive measurement of breathing". IEEE Transactions on Biomedical Engineering. 57 (4): 988–998. doi:10.1109/TBME.2009.2032415.
  11. ^ a b Shastri, Dvijesh; Merla, Arcangelo; Tsiamyrtzis, Panagiotis; Pavlidis, Ioannis (February 2009). "Imaging facial signs of neurophysiological responses". IEEE Transactions on Biomedical Engineering. 56 (2): 477–484. doi:10.1109/TBME.2008.2003265.
  12. ^ Shastri, Dvijesh; Papadakis, Manos; Tsiamyrtzis, Panagiotis; Bass, Barbara; Pavlidis, Ioannis (May 2012). "Perinasal imaging of physiological stress and its affective potential". IEEE Transactions on Affective Computing. 3 (3): 366–378. doi:10.1109/T-AFFC.2012.13.
  13. ^ a b Pavlidis, Ioannis; Eberhardt, Norman; Levine, James (January 3, 2002). "Seeing through the face of deception". Nature. 415: 35. doi:10.1038/415035a.
  14. ^ Tsiamyrtzis, Panagiotis; Dowdall, Jonathan; Shastri, Dvijesh; Pavlidis, Ioannis; Frank, Mark; Ekman, Paul (February 2007). "Imaging facial physiology for the detection of deceit". International Journal of Computer Vision. 71 (2): 197–214. doi:10.1007/s11263-006-6106-y.
  15. ^ Park, Kevin; Suk, Hey; Hwang, Heungsun; Lee, Jang-Han (March 2013). "A functional analysis of deception detection of a mock crime using infrared thermal imaging and the Concealed Information Test". Frontiers in Human Neuroscience. 7. doi:10.3389/fnhum.2013.00070.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  16. ^ Ragoub, Bashar; Zwiggelaar, Reyer (June 2014). "Thermal facial analysis for deception detection". IEEE Transactions on Information Forensics and Securit. 9 (6): 1015–1023. doi:10.1109/TIFS.2014.2317309.
  17. ^ Abouelenien, Mohamed; Pérez-Rosas, Verónica; Mihalcea, Rada; Burzo, Mihai (November 2014). "Deception detection using a multimodal approach". Proceedings of the 16th International Conference on Multimodal Interaction: 58–65. doi:10.1145/2663204.2663229.
  18. ^ Derakhshan, Amin; Mikaeili, Mohammad; Gedeon, Tom; Nasrabadi, Ali Motie (June 2020). "Identifying the optimal features in multimodal deception detection". Multimodal Technologies and Interaction. 4 (2). doi:10.3390/mti4020025.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  19. ^ "Episode 18: Lying". Weird Connections. Discovery. Retrieved 24 April 2023.
  20. ^ "The Agency". CBS. Retrieved 24 April 2023.
  21. ^ Martha, Baer; Katrina, Heron; Oliver, Morton; Evan, Ratliff (January 18, 2005). SAFE: The Race to Protect Ourselves in a Newly Dangerous World. HarperCollins. ISBN 0-06-057715-0.
  22. ^ Pavlidis, Ioannis; Dcosta, Malcolm; Taamneh, Salah; Manser, Mike; Ferris, Thomas; Wunderlich, Robert; Akleman, Ergun; Panagiotis, Tsiamyrtzis (May 2016). "Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors". Scientific Reports. 6. doi:10.1038/srep25651.
  23. ^ Miller, Sara. "Why texting isn't like other kinds of distracted driving". livescience.com. Retrieved 25 April 2023.
  24. ^ Huynh, Tung (May 2021). "Arousal responses to regular acceleration events divide drivers into high and low groups". Proceedings of the 2021 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI'21). doi:10.1145/3411763.3451809.
  25. ^ Eliot, Lance (May 28, 2021). "New research says human drivers are accelarousal-prone which is especially eye-catching for AI self-driving cars". Forbes. Retrieved 25 April 2023.


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