hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Compact orientation and frequency estimation with applications in biometrics: Biometrics on the orientation express
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
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: urn:nbn:se:hh:diva-28205ISBN: 978-91-87045-21-9 ISBN: 978-91-87045-20-2 OAI: oai:DiVA.org:hh-28205DiVA: diva2:811139
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
List of papers
1. Ground truth and evaluation for latent fingerprint matching
Open this publication in new window or tab >>Ground truth and evaluation for latent fingerprint matching
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
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, ISSN 2160-7508 ; 2012
Keyword
SD27, latent, fingerprint
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-19621 (URN)10.1109/CVPRW.2012.6239220 (DOI)2-s2.0-84864967905 (Scopus ID)978-146731611-8 (ISBN)
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
2. Dense frequency maps by Structure Tensor and logarithmic scale space: application to forensic fingerprints
Open this publication in new window or tab >>Dense frequency maps by Structure Tensor and logarithmic scale space: application to forensic fingerprints
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Increasingly, reliable absolute frequency and orientation maps are needed, e.g. for image enhancement. Less studied is however the mutual dependence of both maps, and how to estimate them when none is known initially. We introduce a logarithmic scale space generated by the trace of Structure Tensor to study the relationship. The scale space is non-linear and absolute frequency estimation is reduced to an orientation estimation in it. We show that this offers significant advantages, including construction of efficient estimation methods, using Structure Tensor yielding dense maps of absolute frequency as well as orientation. In fingerprints, both maps can successively improve each other, combined in an image enhancement scheme via Gabor filtering. We verify that the suggested method compares favorably with state of the art, using forensic fingerprints recognition as test bed, and using test images where the ground truth is known. Furthermore, we suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. We present new evidence that the neighborhoods of the absolute frequency map are useful attributes of minutiae. In experiments, we use public data sets to support the conclusions.

Keyword
Structure Tensor, Image enhancement
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-28206 (URN)
Available from: 2015-05-08 Created: 2015-05-06 Last updated: 2017-09-27Bibliographically approved
3. Keypoint Description by Symmetry Assessment–Applications in Biometrics
Open this publication in new window or tab >>Keypoint Description by Symmetry Assessment–Applications in Biometrics
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We present a model-based feature extractor to describe neighborhoods around keypoints by finite expansion, estimating the spatially varying orientation by harmonic functions. The iso-curves of such functions are highly symmetric w.r.t. the origin (a keypoint) and the estimated parameters have well defined geometric interpretations. The origin is also a unique singularity of all harmonic functions, helping to determine thel ocation of a keypoint precisely, whereas the functions describe the object shape of the neighborhood. This is novel and complementary to traditional texture features which describe texture shape properties i.e. they are purposively invariant to translation (within a texture). We report on experiments of verification and identification of keypoints in forensic fingerprints by using publicly available data (NIST SD27), and discuss the results in comparison to other studies. These support our conclusions that the novel features can equip single cores or single minutia with a significant verification power at 19% EER, and an identification power of 24-78% for ranks of 1-20. Additionally, we report verification results of periocular biometrics using near infrared images, reaching an EER performance of 13%, whichis comparable to the state of the art. More importantly, fusion of two systems, our and texture features (Gabor), result in a measurable performance improvement. We report reduction ofthe EER to 9%, supporting the view that the novel features capture relevant visual

Keyword
Biometrics, keypoints
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-28238 (URN)
Available from: 2015-05-11 Created: 2015-05-11 Last updated: 2017-09-27Bibliographically approved

Open Access in DiVA

fulltext(8848 kB)413 downloads
File information
File name FULLTEXT01.pdfFile size 8848 kBChecksum SHA-512
13eb29ad257f248f8ebe3c1e61511fa79e6f3f1041586472fe51b17250a97b6ace5c2e9f7cfa16419b11194f6005b833caac8b7a925c54bc960473600f2923aa
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Mikaelyan, Anna
By organisation
CAISR - Center for Applied Intelligent Systems Research
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 413 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 427 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf