Classification of startle eyeblink metrics using neural networks

Authors:
Christopher T. Lovelace;Reza Derakhshani;Sriram Pavan Kumar Tankasala;Diane L. Filion
Affiliations:
University of Missouri - Kansas City, Kansas City, MO;University of Missouri - Kansas City, Kansas City, MO;University of Missouri - Kansas City, Kansas City, MO;University of Missouri - Kansas City, Kansas City, MO
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 4
Cited 1

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society

Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM

Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)

Eyelid contour detection and tracking for startle research related eye-blink measurements from high-speed video records

Computer Methods and Programs in Biomedicine

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Abstract

In this paper, we show the feasibility of using high-speed video for measurement of startle eyeblinks as a new augmentative modality for biometric security, as blinks can reveal emotional states of interest in security screenings using nonintrusive measurements. Using neural network as classifiers, this initial study shows that upper eyelid tracking at 250 frames per second can categorize startle blinks with accuracies comparable to those of the well-established but intrusive EMG-based measures of muscles in charge of eyelid closure.