Quantitative Analytical Methods for Real Time Lie Detection Using Eye Gaze and Biometric Sensors

Quantitative Analytical Methods for Real Time Lie Detection Using Eye Gaze and Biometric Sensors

Presenter(s)

Tanner Cuttone, Sean Davy, Nathaniel Doll

Comments

9:00-10:15, Kennedy Union Ballroom

Description

This poster provides a summary of an IRB approved research study on the optical response of the human eye using a GazePoint eye tracking system and biometrics hardware. Pupil dilation, gaze position, blink rate, and reaction time were recorded for human subjects in response to various visual and auditory stimuli on a computer screen. In addition, EEG, heart rate, blood pressure, and galvanic skin response were recorded using a suite of simultaneous biosensors. The experimental tasks were designed with varying levels of complexity and included both memory-recall and computational tasks for truth and deception scenarios. The overall aim of this study was to identify establish baseline physiological data sets across multiple demographics, which can be used in the future to advance forensic diagnostic methodologies using quantitative analysis and machine learning for various types of neuroscience applications, including lie detection.

Publication Date

4-23-2025

Project Designation

Independent Research

Primary Advisor

Amy T. Neidhard-Doll

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium, School of Engineering

Institutional Learning Goals

Scholarship; Practical Wisdom; Community

Quantitative Analytical Methods for Real Time Lie Detection Using Eye Gaze and Biometric Sensors

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