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Automatic Registration of Serial Cerebral Angiography: A Comparative Review Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Authentication-Based on Biomechanics of Finger Movements Captured Using Optical Motion-Capture

Authors : Brittany Lewis, Christopher J. Nycz, Gregory S. Fischer, Krishna K. Venkatasubramanian

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

In this paper, we propose an authentication approach based on the uniqueness of the biomechanics of finger movements. We use an optical-marker-based motion-capture as a preliminary setup to capture goniometric (joint-related) and dermatologic (skin-related) features from the flexion and extension of the index and middle fingers of a subject. We use this information to build a personalized authentication model for a given subject. Analysis of our approach using finger motion-capture from 8 subjects, using reflective tracking markers placed around the joints of index and middle fingers of the subjects shows its viability. In this preliminary study, we achieve an average equal error rate (EER)—when false accept rate and false reject rate are equal—of 6.3% in authenticating a subject immediately after training the authentication model and 16.4% ERR after a week.

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Footnotes
1
Equal error rate is the value where the false accept and false reject rates for a model are equal.
 
2
We use only the first session’s data for training because that’s how typical authentication modality works. We enroll (in our case train the model) once and then subsequently authenticate repeatedly.
 
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Metadata
Title
Authentication-Based on Biomechanics of Finger Movements Captured Using Optical Motion-Capture
Authors
Brittany Lewis
Christopher J. Nycz
Gregory S. Fischer
Krishna K. Venkatasubramanian
Copyright Year
2018
Book
Advances in Visual Computing

Print ISBN: 978-3-030-03800-7

Electronic ISBN: 978-3-030-03801-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-03801-4_16

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