Brown, DaneBradshaw, K2017-06-072017-06-072016-09Brown, D. and Bradshaw, K. 2016. Extended feature-fusion guidelines to improve image-based multi-modal biometrics. Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT), 26-28 September 2016, Johannesburg, South Africa. DOI: http://dx.doi.org/10.1145/2987491.2987512978-1-4503-4805-8DOI: http://dx.doi.org/10.1145/2987491.2987512http://dl.acm.org/citation.cfm?id=2987512http://www.cs.uwc.ac.za/~dbrown/5.pdfhttp://hdl.handle.net/10204/9238Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT), 26-28 September 2016, Johannesburg, South Africa. 2016 Copyright held by the owner/author(s).The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature-level for improved human identification accuracy. Feature-fusion guidelines, proposed in recent work, are extended by adding the palmprint modality and the support vector machine classifier. Guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature-level to reduce the equal error rate on the SDUMLA and IITD datasets, using a novel feature-fusion methodology.enMulti-modal biometricsFeature-level fusionFingerprintsPalmprintsImage processingComputing methodologiesBiometricsConference PresentationBrown, D., & Bradshaw, K. (2016). Extended feature-fusion guidelines to improve image-based multi-modal biometrics. ACM Digital Library. http://hdl.handle.net/10204/9238Brown, Dane, and K Bradshaw. "Extended feature-fusion guidelines to improve image-based multi-modal biometrics." (2016): http://hdl.handle.net/10204/9238Brown D, Bradshaw K, Extended feature-fusion guidelines to improve image-based multi-modal biometrics; ACM Digital Library; 2016. http://hdl.handle.net/10204/9238 .TY - Conference Presentation AU - Brown, Dane AU - Bradshaw, K AB - The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature-level for improved human identification accuracy. Feature-fusion guidelines, proposed in recent work, are extended by adding the palmprint modality and the support vector machine classifier. Guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature-level to reduce the equal error rate on the SDUMLA and IITD datasets, using a novel feature-fusion methodology. DA - 2016-09 DB - ResearchSpace DP - CSIR KW - Multi-modal biometrics KW - Feature-level fusion KW - Fingerprints KW - Palmprints KW - Image processing KW - Computing methodologies KW - Biometrics LK - https://researchspace.csir.co.za PY - 2016 SM - 978-1-4503-4805-8 T1 - Extended feature-fusion guidelines to improve image-based multi-modal biometrics TI - Extended feature-fusion guidelines to improve image-based multi-modal biometrics UR - http://hdl.handle.net/10204/9238 ER -