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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Notes
- 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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Acknowledgments
The authors would like to thank Tess Meier who helped with the data collection for this work. This work is supported by the defense health program grant DHP W81XWH-15-C-0030.
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Lewis, B., Nycz, C.J., Fischer, G.S., Venkatasubramanian, K.K. (2018). Authentication-Based on Biomechanics of Finger Movements Captured Using Optical Motion-Capture. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_16
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