A Novel Biometric based on Neural Representations of Synergistic Hand Grasps

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dc.contributor.authorPatel, Vrajeshri
dc.contributor.authorBurns, Martin
dc.contributor.authorFlorescu, Ionut
dc.contributor.authorChandramouli, Rajarathnam
dc.contributor.authorVinjamuri, Ramana
dc.date.accessioned2021-05-19T18:47:04Z
dc.date.available2021-05-19T18:47:04Z
dc.date.issued2017-11-29
dc.descriptionFuture Technologies Conference (FTC) 2017 29-30 November 2017| Vancouver, Canadaen_US
dc.description.abstract—To meet the growing need of robust and secure identity verification systems, a new biometric based on neural representations of synergistic hand grasps is proposed here. In this preliminary study five subjects were asked to perform six synergistic hand grasps that are shared most often in common activities of daily living. Their scalp electroencephalographic (EEG) signals were analyzed using 20 scalp electrodes. In our previous work, we found that hand kinematics of these synergistic grasps showed potential as a biometric. In the current work, we asked if the neural representations of these synergistic grasps can provide a unique signature to be a biometric. The results show that across 300 entries, the system, in its best configuration, achieved an accuracy of 92.2% and an EER of ~4.7% when tasked with identifying these five individuals. The implications of these preliminary results and applications in the near future are discussed. We believe that this study could lead to the development of a novel biometric as a potential future technology.en_US
dc.description.urihttps://saiconference.com/Downloads/FTC2017/Proceedings/113_Paper_271-A_Novel_Biometric_based_on_Neural_Representations.pdfen_US
dc.format.extent6 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2l36l-y9op
dc.identifier.citationPatel, Vrajeshri; Burns, Martin; Florescu, Ionut; Chandramouli, Rajarathnam; Vinjamuri, Ramana; A Novel Biometric based on Neural Representations of Synergistic Hand Grasps; Future Technologies Conference (FTC) 2017. https://saiconference.com/Downloads/FTC2017/Proceedings/113_Paper_271-A_Novel_Biometric_based_on_Neural_Representations.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/21570
dc.language.isoen_USen_US
dc.publisherScience and Information Conferencesen_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.subjectbiometricsen_US
dc.subjecthand synergiesen_US
dc.subjectquadratic discriminant classifieren_US
dc.subjectelectroencephalography (EEG)en_US
dc.subjectfeature extractionen_US
dc.titleA Novel Biometric based on Neural Representations of Synergistic Hand Graspsen_US
dc.typeTexten_US

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