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Keypoint Description by Symmetry Assessment–Applications in Biometrics
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-1400-346X
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4929-1262
(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 [en]
Biometrics, keypoints
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-28238OAI: oai:DiVA.org:hh-28238DiVA: diva2:811133
Available from: 2015-05-11 Created: 2015-05-11 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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