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Biometrics via Oculomotor Plant Characteristics: Impact of Parameters in Oculomotor Plant Model by Oleg Komogortsev; Corey Holland; Alex Karpov; Larry R. Price is a Computer Science article available to read on EtoBox.

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This article proposes and evaluates a novel biometric approach utilizing the internal, nonvisible, anatomical structure of the human eye. The proposed method estimates the anatomical properties of the human oculomotor plant from the measurable properties of human eye movements, utilizing a two-dimensional linear homeomorphic model of the oculomotor plant. The derived properties are evaluated within a biometric framework to determine their efficacy in both verification and identification scenarios. The results suggest that the physical properties derived from the oculomotor plant model are capable of achieving 20.3% equal error rate and 65.7% rank-1 identification rate on high-resolution equipment involving 32 subjects, with biometric samples taken over four recording sessions; or 22.2% equal error rate and 12.6% rank-1 identification rate on low-resolution equipment involving 172 subjects, with biometric samples taken over two recording sessions.

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Author

Oleg Komogortsev; Corey Holland; Alex Karpov; Larry R. Price

Publisher

ACM

Published

2014

Language

EN

Field

Computer Science (Physical Sciences)