Recognizing song-based blink patterns: applications for restricted and universal access

Authors:
Tracy Westeyn;Thad Starner
Affiliations:
GVU, College of Computing, Georgia Institute of Technology, Atlanta, GA;GVU, College of Computing, Georgia Institute of Technology, Atlanta, GA
Venue:
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Year:
2004

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Abstract

We introduce a novel system for recognizing patterns of eye blinks for use in assistive technology interfaces and security systems. First, we present a blink-based interface for controlling devices. Well known songs are used as the cadence for the blinked patterns. Our system distinguishes between ten similar patterns with 99.0% accuracy. Second, we present a method for identifying individual people based on the characteristics of how they perform a specific pattern (their "blinkprint"). This technique could be used in conjunction with face recognition for security systems. We are able to distinguish between nine individuals with 82.02% accuracy based solely on how they blink the same pattern.