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

    Share

    COinS