Rucker, J.C.: Normal and abnormal lid function. Vrij, A.: Deception and truth detection when analyzing nonverbal and verbal cues. Advances in Intelligent Systems and Computing, vol. (eds.) Advances in Neuroergonomics and Cognitive Engineering.
Errorprovider blink rate verification#
Monaro, M., Gamberini, L., Sartori, G.: Identity verification using a kinematic memory detection technique. In: Proceedings of the 12th International Conference on Availability, Reliability and Security, ARES 2017, Reggio Calabria, Italy (2017). Monaro, M., Spolaor, R., QianQian, L., Conti, M., Gamberini, L., Sartori, G.: Type me the truth!: Detecting deceitful users via keystroke dynamics. Suchotzki, K., Verschuere, B., Van Bockstaele, B., Ben-Shakhar, G., Crombez, G.: Lying takes time: a meta-analysis on reaction time measures of deception. Monaro, M., Gamberini, L., Sartori, G.: The detection of faked identity using unexpected questions and mouse dynamics. Monaro, M., Galante, C., Spolaor, R., Li, Q.Q., Gamberini, L., Conti, M., Sartori, G.: Covert lie detection using keyboard dynamics.
Lancaster, G.L.J., Vrij, A., Hope, L., Waller, B.: Sorting the liars from the truth tellers: the benefits of asking unanticipated questions on lie detection. Hartwig, M., Granhag, P.A., Strçmwall, L.: Guilty and innocent suspects’ strategies during interrogations. Vrij, A., Leal, S., Granhag, P.A., Mann, S., Fisher, R.P., Hillman, J., Sperry, K.: Outsmarting the liars: the benefit of asking unanticipated questions. Walczyk, J.J., Igou, F.P., Dixon, A.P., Tcholakian, T.: Advancing lie detection by inducing cognitive load on liars: a review of relevant theories and techniques guided by lessons from polygraph-Based approaches. Paas, F., Renkl, A., Sweller, J.: Cognitive load theory and instructional design: recent developments. Vrij, A., Fisher, R.P., Blank, H.: A cognitive approach to lie detection: a meta-analysis. Vrij, A., Fisher, R., Mann, S., Leal, S.: A cognitive load approach to lie detection. Training machine learning classification models on these features, an accuracy up to 70% in identifying liars and truth-tellers was obtained. Moreover, the automatic blink detector was applied to 68 videos of people who were lying or telling the truth about a past holiday, testing the difference between the two groups in terms of blink rate and response timing. Results demonstrated that the automatic blink detector reached an accuracy similar to the electrooculogram in detecting the blink rate. In this paper, we propose a new approach to automatically detect eye blinking based on a computer vision algorithm, which does not require any expensive technology to record data. However, it is difficult to apply in the real forensic scenario as very complex techniques to record eye blinking are usually needed (e.g., electrooculography, eye tracker technology). Eye-blink is a sensitive index of cognitive load and some studies have reported that it can be a useful cue for detecting deception.
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