Brown, KBradshaw, K2017-06-072017-06-072016-05Brown, 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.2016.7568927978-1-5090-0770-7DOI: 10.1109/THS.2016.7568927http://www.cs.uwc.ac.za/~dbrown/2.pdfhttp://ieeexplore.ieee.org/document/7568927/http://hdl.handle.net/10204/9240Copyright: 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.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.enFaceFingerprintsFeature-levelsMulti-modal biometricsConference PresentationBrown, K., & Bradshaw, K. (2016). A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification. IEEE. http://hdl.handle.net/10204/9240Brown, K, and K Bradshaw. "A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification." (2016): http://hdl.handle.net/10204/9240Brown 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 .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 -