Towards regional fusion for high-resolution palmprint recognition
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
IEEEDate
2013-12-01Citation
10.1109/SIBGRAPI.2013.56
2013 IEEE Conference on Graphics, Patterns and Images (SIBGRAPI), IEEE, 2013. 357 - 361
ISSN
1530-1834DOI
10.1109/SIBGRAPI.2013.56Funded by
R. Wang and R. Krish are supported by Marie Curie Fellowships under project BBfor2 (FP7-ITN-238803). This work has also been partially supported by Spanish Guardia Civil, “Cátedra UAM-Telefónica”, and projects Bio-Shield (TEC2012-34881) and Contexts (S2009/TIC-1485).Project
Comunidad de Madrid. S2009/TIC-1485/CONTEXTS; info:eu-repo/grantAgreement/EC/FP7/238803Editor's Version
http://dx.doi.org/10.1109/SIBGRAPI.2013.56Subjects
High resolution palmprints; Regional fusion; TelecomunicacionesNote
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. R. Wang, D. Ramos, J. Fiérrez, R. P. Krish, "Towards Regional Fusion for High-Resolution Palmprint Recognition" in 26th Conference on Graphics, Patterns and Images (SIBGRAPI), Arequipa (Peru), 2013, pp. 357 - 361Rights
© 2013 IEEEAbstract
The existing high resolution palm print matching algorithms essentially follow the minutiae-based fingerprint matching strategy and focus on full-to-full/partial-to-full palm print comparison. These algorithms would face problems when they are applied to forensic palm print recognition where latent marks have much smaller area than full palm prints. Therefore, towards forensic scenarios, we propose a novel matching strategy based on regional fusion for high resolution palm print recognition using regions segmented by major creases features. The matching strategy includes two stages: 1) region-to-region palm print comparison, 2) regional fusion at score level. We first studied regional discriminability of a high resolution palm print under the concept of three regions, i.e., interdigital, hypothenar and thenar, which is the most significant difference between palmprits and fingerprints. Then we implemented regional fusion based on logistic regression at score level using region-to-region comparison scores obtained by a commercial SDK, Mega Matcher 4.0. Significant improvement of recognition accuracy is achieved by regional fusion on a public high resolution palm print database THUPALMLAB. The EER of logistic regression based regional fusion is 0.25%, while the EER of full-to-full palm print comparison is 1%.
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Google Scholar:Wang, Ruifang
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Ramos Castro, Daniel
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Krishnamoorthy, Ram Prasad
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Fiérrez Aguilar, Julián
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