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Using Blink Rate to Detect Deception: A Study to Validate an Automatic Blink Detector and a New Dataset of Videos from Liars and Truth-Tellers

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Human-Computer Interaction. Human Values and Quality of Life (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12183))

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Abstract

Eye-blink is a sensitive index of cognitive load and some studies have reported that it can be a useful cue for detecting deception. 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). 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. Results demonstrated that the automatic blink detector reached an accuracy similar to the electrooculogram in detecting the blink rate. 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. Training machine learning classification models on these features, an accuracy up to 70% in identifying liars and truth-tellers was obtained.

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Authors and Affiliations

  1. University of Padova, Via Venezia 8, 35131, Padua, Italy

    Merylin Monaro, Pasquale Capuozzo, Federica Ragucci, Antonio Maffei, Cristina Scarpazza, Alessandro Angrilli & Giuseppe Sartori

  2. Università degli Studi di Bari Aldo Moro, Via Scipione Crisanzio, 42, 70122, Bari, Italy

    Antonietta Curci

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  1. Merylin Monaro
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Correspondence to Merylin Monaro .

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  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

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Monaro, M. et al. (2020). Using Blink Rate to Detect Deception: A Study to Validate an Automatic Blink Detector and a New Dataset of Videos from Liars and Truth-Tellers. In: Kurosu, M. (eds) Human-Computer Interaction. Human Values and Quality of Life. HCII 2020. Lecture Notes in Computer Science(), vol 12183. Springer, Cham. https://doi.org/10.1007/978-3-030-49065-2_35

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  • DOI: https://doi.org/10.1007/978-3-030-49065-2_35

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