Improving radial triangulation-based forensic palmprint recognition according to point pattern comparison by relaxation
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
IEEEDate
2012Citation
10.1109/ICB.2012.6199788
2012 5th IAPR International Conference on Biometrics, ICB. IEEE, 2012. 427 - 432
ISBN
978-1-4673-0396-5 (print); 978-1-4673-0397-2 (online)DOI
10.1109/ICB.2012.6199788Funded by
The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement number 238803; the DGUI of Comunidad Autónoma de Madrid and Universidad Autónoma de Madrid via grant CCG10-UAM/TIC-5792; Departamento de Identificación of Guardia Civil Española.Project
info:eu-repo/grantAgreement/EC/FP7/238803Editor's Version
http://dx.doi.org/10.1109/ICB.2012.6199788Subjects
Computer forensics; Feature extraction; Fingerprint identification; Image enhancement; Image segmentation; Palmprint recognition; Regression analysis; 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, "Improving radial triangulation-based forensic palmprint recognition according to point pattern comparison by relaxation" in 5th IAPR International Conference Biometrics (ICB), New Delhi (India), 2012, 427 - 432Rights
© 2012 IEEEAbstract
Forensic palmprint recognition, which mainly deals with high-resolution palmprints and latent-to-full palmprint comparison, has aroused research highlights because of the increased use of the evidence of palmprints in forensics. There are some in-depth works on high-resolution palmprint preprocessing (i.e., segmentation and enhancement) and feature extraction. However, few works on latent-to-full palmprint comparison have been done. Recently, radial triangulation-based latent-to-full palmprint comparison algorithm was proposed as it has been proposed for forensic likelihood ratio computation using fingerprints, and proved to have identification usability and efficiency for palmprint comparison. In this work, we generalize point pattern comparison by relaxation to minutiae-based palmprint recognition and improve the latent-to-full palmprint comparison algorithm based on radial triangulation. Firstly, local minutiae comparison is modified according to novel point pattern comparison method and global minutiae comparison is based on centroid distribution. Then logistic regression learning is used for comparison score computation. Performance of the proposed algorithm is evaluated on forensic databases including 22 latent palmprints from real cases and 8680 full palmprints from criminal investigation field. Experimental results show the improvement on identification accuracy and efficiency of our approach. A rank-1 identification rate of 69% is achieved, compared with 63% of previous radial triangulation-based.
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Google Scholar:Wang, Ruifang
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Ramos Castro, Daniel
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Fiérrez Aguilar, Julián
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