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Ground truth and evaluation for latent fingerprint matching
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0002-4929-1262
2012 (English)In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012, Piscataway, NJ: IEEE Computer Society, 2012, 83-88 p.Conference paper, Published paper (Refereed)
Abstract [en]

In forensic fingerprint studies annotated databases is important for evaluating the performance of matchers as well as for educating fingerprint experts. We have estab- lished ground truths of minutia level correspondences for the publicly available NIST SD27 data set, whose minutia have been extracted by forensic fingerprint experts. We per- formed verification tests with two publicly available minutia matchers, Bozorth3 and k-plet, yielding Equal Error Rates of 36% and 40% respectively, suggesting that they have sim- ilar (poor) ability to separate a client from an impostor in latent versus tenprint queries. However, in an identifica- tion scenario, we found performance advantage of k-plet over Bozorth3, suggesting that the former can rank the sim- ilarities of fingerprints better. Regardless of the matcher, the general poor performance is a confirmation of previous findings related to latent vs tenprint matching. A finding influencing future practice is that the minutia level match- ing errors in terms of FA and FR may not be balanced (not equally good) even if FA and FR have been chosen to be so at finger level.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2012. 83-88 p.
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, ISSN 2160-7508 ; 2012
Keyword [en]
SD27, latent, fingerprint
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:hh:diva-19621DOI: 10.1109/CVPRW.2012.6239220Scopus ID: 2-s2.0-84864967905ISBN: 978-146731611-8 OAI: oai:DiVA.org:hh-19621DiVA: diva2:552559
Conference
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, June 16-21, 2012
Note

©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Available from: 2012-10-04 Created: 2012-09-14 Last updated: 2017-09-27Bibliographically approved
In thesis
1. Compact orientation and frequency estimation with applications in biometrics: Biometrics on the orientation express
Open this publication in new window or tab >>Compact orientation and frequency estimation with applications in biometrics: Biometrics on the orientation express
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Automatic feature extraction still remains a relevant image and signal processing problem even tough both the field and technologies are developing rapidly. Images of low quality, where it is extremely difficult to reliably process image information automatically, are of special interest. To such images we can refer forensic fingerprints, which are left unintentionally on different surfaces andare contaminated by several of the most difficult noise types. For this reason, identification of fingerprints is mainly based on the visual skills of forensic examiners. We address the problem caused by low quality in fingerprints by connecting different sources of information together, yielding dense frequency and orientation maps in an iterative scheme. This scheme comprises smoothing ofthe original, but only along, ideally never across, the ridges. Reliable estimation of dense maps allows to introduce a continuous fingerprint ridge counting technique. In fingerprint scenario the collection of irrefutable tiny details, e.g. bifurcation of ridges, called minutiae, is used to tie the pattern of such points and their tangential directions to the finger producing the pattern. This limited feature set, location and direction of minutiae, is used in current AFIS systems, while fingerprint examiners use the extended set of features, including the image information between the points. With reasonably accurate estimationsof dense frequency and orientation maps at hand, we have been able to propose a novel compact feature descriptor of arbitrary points. We have used these descriptors to show that the image information between minutiae can be extracted automatically and be valuable for identity establishment of forensic images even if the underlying images are noisy. We collect and compress the image information in the neighborhoods of the fine details, such as minutiae, to vectors, one per minutia, and use the vectors to "color" the minutiae. When matching two patterns (of minutiae) even the color of the minutia must match to conclude that they come from the same identity. This feature development has been concentrated and tested on forensic fingerprint images. However, we have also studied an extension of its application area to other biometrics, periocular regions of faces. This allowed us to test the persistence of automatically extracted features across different types of imagesand image qualities, supporting its generalizability.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2015. 69 p.
Series
Halmstad University Dissertations, 10
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-28205 (URN)978-91-87045-21-9 (ISBN)978-91-87045-20-2 (ISBN)
Public defence
2015-04-17, Wigforssalen, Visionen, Kristian IV:s väg 3, 301 18, Halmstad, 10:15 (English)
Opponent
Supervisors
Available from: 2015-05-11 Created: 2015-05-06 Last updated: 2017-09-27Bibliographically approved

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