Detecting deception via eyeblink frequency modulation

Author:

Perelman Brandon S.1

Affiliation:

1. Michigan Technological University, Department of Cognitive and Learning Sciences, Houghton, MI, USA

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference32 articles.

1. A comparison of methods for ERP assessment in a P300-based GKT;Abootalebi;International Journal of Psychophysiology,2006

2. A concealed information test with multimodal measurement;Ambach;International Journal of Psychophysiology,2010

3. The guilty knowledge test (GKT) as an application of psychophysiology: future prospects and obstacles;Ben-Shakar,2002

4. Lying about facial recognition: an fMRI study;Bhatt;Brain and Cognition,2009

5. A new approach to detecting deception using learning theory (U.S. Army Research Office Report No. 45232-LS-DRP.1). Available at DTIC Online Website: http://www.dtic.mil/dtic/tr/fulltext/u2/a455533.pdf

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1. Robust Eye Blink Detection Using Dual Embedding Video Vision Transformer;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

2. LieVis: A Visual Interactive Dashboard for Lie Detection Using Machine Learning and Deep Learning Techniques;2023 26th International Conference on Computer and Information Technology (ICCIT);2023-12-13

3. “Blinks in the Dark”: Blink Estimation With Domain Adversarial Training (BEAT) Network;IEEE Transactions on Consumer Electronics;2023-08

4. Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety;Electronics;2022-10-04

5. In the blink of an eye: Quantitative blink dynamics predict deceptive personality traits in forensic interviews;Personality and Individual Differences;2021-07

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