Detection of Deception through Eye Detect System (EDS)

Abstract

The purpose of the present study was to investigate the efficiency of the detection of deception tool Eye Detect System (EDS) in identifying different types of deceptions. In this study, a sample of 18 participants aged between 18 to 60 years including 9 males and 9 females who had studied English at least till class 12 were taken. They were divided into 3 groups namely Group A (lying group), Group B (misleading group) and Group C (truthful group) randomly. Group A was made to commit the mock cybercrime but were instructed to deny the same while taking the EDS test. Group B was given a written report with screenshots along with its screen recording of the mock cybercrime and they were instructed to admit committing the cybercrime while taking the EDS test. Group C were only given a written report with screenshots of the mock cybercrime and were instructed to be truthful while taking the EDS test.  And then their data was recorded on the Eye Detect System using the Direct Lie Comparison Test (DLCT) type. The data analysis was done using Kruskal Wallis (H) test and Dunn Test. The finding of the present study showed that the detection of deception tool Eye Detect System is efficient to differentiate between the different types of deceptions. From the present study, it can be concluded that the detection of deception tool Eye Detect System is efficient in detecting types of deception, as there is limited research in this field, there is scope for more research in this field and prevent innocent people getting punished due to false confessions under pressure or otherwise.