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A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification

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dc.contributor.author Brown, K
dc.contributor.author Bradshaw, K
dc.date.accessioned 2017-06-07T08:02:48Z
dc.date.available 2017-06-07T08:02:48Z
dc.date.issued 2016-05
dc.identifier.citation Brown, D. and Bradshaw, K. 2016. A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification. 2016 IEEE Symposium on Technologies for Homeland Security (HST), 10-12 11 May 2016, Waltham, MA, USA. DOI: 10.1109/THS.2016.7568927 en_US
dc.identifier.isbn 978-1-5090-0770-7
dc.identifier.uri DOI: 10.1109/THS.2016.7568927
dc.identifier.uri http://www.cs.uwc.ac.za/~dbrown/2.pdf
dc.identifier.uri http://ieeexplore.ieee.org/document/7568927/
dc.identifier.uri http://hdl.handle.net/10204/9240
dc.description Copyright: 2016 IEEE. Due to copyright restrictions, the attached PDF file contains the accepted version of the full text item. For access to the published version, kindly consult the publisher's website. en_US
dc.description.abstract The lack of multi-biometric fusion guidelines at the feature-level are addressed in this work. A feature-fusion framework is geared toward improving human identification accuracy for both single and multiple biometrics. The foundation of the framework is the improvement over a state-of-the-art uni-modal biometric verification system, which is extended into a multi-modal identification system. A novel multi-biometric system is thus designed based on the framework, which serves as fusion guidelines for multi-biometric applications that fuse at the feature-level. This framework was applied to the face and fingerprint to achieve a 91.11% recognition accuracy when using only a single training sample. Furthermore, an accuracy of 99.69% was achieved when using five training samples. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;17651
dc.subject Face en_US
dc.subject Fingerprints en_US
dc.subject Feature-levels en_US
dc.subject Multi-modal biometrics en_US
dc.title A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Brown, K., & Bradshaw, K. (2016). A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification. IEEE. http://hdl.handle.net/10204/9240 en_ZA
dc.identifier.chicagocitation Brown, K, and K Bradshaw. "A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification." (2016): http://hdl.handle.net/10204/9240 en_ZA
dc.identifier.vancouvercitation Brown K, Bradshaw K, A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification; IEEE; 2016. http://hdl.handle.net/10204/9240 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Brown, K AU - Bradshaw, K AB - The lack of multi-biometric fusion guidelines at the feature-level are addressed in this work. A feature-fusion framework is geared toward improving human identification accuracy for both single and multiple biometrics. The foundation of the framework is the improvement over a state-of-the-art uni-modal biometric verification system, which is extended into a multi-modal identification system. A novel multi-biometric system is thus designed based on the framework, which serves as fusion guidelines for multi-biometric applications that fuse at the feature-level. This framework was applied to the face and fingerprint to achieve a 91.11% recognition accuracy when using only a single training sample. Furthermore, an accuracy of 99.69% was achieved when using five training samples. DA - 2016-05 DB - ResearchSpace DP - CSIR KW - Face KW - Fingerprints KW - Feature-levels KW - Multi-modal biometrics LK - https://researchspace.csir.co.za PY - 2016 SM - 978-1-5090-0770-7 T1 - A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification TI - A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification UR - http://hdl.handle.net/10204/9240 ER - en_ZA


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