Revealing Concealed Information Using Sophisticated Analysis of Eye-Tracking Data

Pertzov Yoni , HUJI, Faculty of Social Sciences, Psychology

Gershon Ben-Shakhar, Department of Psychology, the Hebrew University of Jerusalem


Innovation summary

  • We have developed an accurate technique for detecting deceitful attempts to deny recognition of a familiar face in a photograph. Based on eye-tracking technology, the innovative technique tracks eye movements – often involuntary – as a reaction to viewing a picture of an acquaintance.
  •  The technique serves as a vital tool for enhancing CIT (Concealed Information Test) – a proven method used in polygraph tests, or during interrogations, whereby key questions are embedded among several unrelated control questions, for uncovering attempts to conceal information.
  •  This method, which can be applied without the awareness of the tracked subject, fills an acute, increasing need for an effective method of revealing associations of individuals with terrorists, and thereby preventing terror attacks.


The technology utilizes a standard, non-intrusive video-based eye-tracker which tracks the point of gaze on the screen. In addition, several specially-developed machine-learning algorithms enable accurate classification of familiar and unfamiliar faces.

When a subject is shown photos of possible acquaintances, the gaze behavior is analyzed, revealing the following patterns:

  • Familiar faces initially attract the viewer’s fixation before moving to a different face. This fixation is followed by a strong tendency not to return to it (the ‘repulsion effect’). During the first second of observation gaze is directed significantly more towards familiar faces, than towards unfamiliar faces.
  • Unfamiliar faces During the whole duration of observation, gaze is directed significantly more towards unfamiliar faces, than towards familiar faces.

The technique has an accuracy rate of over 91% for detecting familiar faces.

Furthermore, the technique demonstrates a higher detection efficiency in differentiating between participants that are familiar with one of the faces and participants that are unfamiliar with all of the faces. This classification accuracy exceeds all traditional CIT based on autonomic (e.g. skin conductance) as well as neuroimaging measures.



  • More accurate technique for detecting deception, compared to measuring other physiological responses such as autonomic and neuroimaging measures
  • Portable, convenient method that is quicker than monitoring other physiological parameters, such as increased skin conductance and neuroimaging methods
  • Could be applied without the tracked individual’s awareness
  • No need to connect any peripheral equipment, such as electrodes
  • Can be implemented by anyone, requiring minimal technical training  

Development milestones

  • The technology was implemented in two experiments, in which photographs of human faces were viewed. In these experiments, participants were requested to memorize several faces, including personal Facebook friends of theirs, and were instructed to try and conceal familiarity with any faces they recognized in the photos.
  • Future research will include neuronal and cognitive mechanisms underlying the familiarity-related ‘attraction’ vs. ‘repulsion’ effects, and investigating the correlation between LTM (long-term memory) and attention.
  •  Additional research will focus on tracking eye movements using other stimuli, such as photos of geographic locations or objects, combined with other measurable physiological responses.
  •  We will also examine whether this technique – as most physiological measures – is vulnerable to countermeasures.
  •  So far GIF young grant has been received.


  • Secret services, intelligence and security agencies
  • Airports
  • Police agencies and Interpol



Patent Status

Granted US 11020034

Contact for more information:

Anna Pellivert
Contact ME: