Hand Grasping Synergies As Biometrics

dc.contributor.authorPatel, Vrajeshri
dc.contributor.authorThukral, Poojita
dc.contributor.authorBurns, Martin K.
dc.contributor.authorFlorescu, Ionut
dc.contributor.authorChandramouli, Rajarathnam
dc.contributor.authorVinjamuri, Ramana
dc.date.accessioned2021-05-17T16:55:51Z
dc.date.available2021-05-17T16:55:51Z
dc.date.issued2017-05-02
dc.description.abstractRecently, the need for more secure identity verification systems has driven researchers to explore other sources of biometrics. This includes iris patterns, palm print, hand geometry, facial recognition, and movement patterns (hand motion, gait, and eye movements). Identity verification systems may benefit from the complexity of human movement that integrates multiple levels of control (neural, muscular, and kinematic). Using principal component analysis, we extracted spatiotemporal hand synergies (movement synergies) from an object grasping dataset to explore their use as a potential biometric. These movement synergies are in the form of joint angular velocity profiles of 10 joints. We explored the effect of joint type, digit, number of objects, and grasp type. In its best configuration, movement synergies achieved an equal error rate of 8.19%. While movement synergies can be integrated into an identity verification system with motion capture ability, we also explored a camera-ready version of hand synergies—postural synergies. In this proof of concept system, postural synergies performed well, but only when specific postures were chosen. Based on these results, hand synergies show promise as a potential biometric that can be combined with other hand-based biometrics for improved security.en_US
dc.description.sponsorshipThe authors would like to thank the Office of Innovation and Entrepreneurship and Department of Biomedical Engineering, Chemistry, and Biological Sciences of the Stevens Institute of Technology for their continued support of ongoing research.en_US
dc.description.urihttps://www.frontiersin.org/articles/10.3389/fbioe.2017.00026/fullen_US
dc.format.extent11 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2uzl0-s4ld
dc.identifier.citationPatel V, Thukral P, Burns MK, Florescu I, Chandramouli R and Vinjamuri R (2017) Hand Grasping Synergies As Biometrics. Front. Bioeng. Biotechnol. 5:26. doi: 10.3389/fbioe.2017.00026en_US
dc.identifier.urihttps://doi.org/10.3389/fbioe.2017.00026
dc.identifier.urihttp://hdl.handle.net/11603/21554
dc.language.isoen_USen_US
dc.publisherFrontiers Mediaen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 international*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleHand Grasping Synergies As Biometricsen_US
dc.typeTexten_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
fbioe-05-00026.pdf
Size:
1.39 MB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description: