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Kollreider, Klaus
Publications (10 of 15) Show all publications
Alonso-Fernandez, F., Bigun, J., Fierrez, J., Fronthaler, H., Kollreider, K. & Ortega-Garcia, J. (2009). Fingerprint Recognition. In: Dijana Petrovska-Delacrétaz, Gérard Chollet, Bernadette Dorizzi (Ed.), Guide to Biometric Reference Systems and Performance Evaluation: (pp. 51-88). London: Springer London
Open this publication in new window or tab >>Fingerprint Recognition
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2009 (English)In: Guide to Biometric Reference Systems and Performance Evaluation / [ed] Dijana Petrovska-Delacrétaz, Gérard Chollet, Bernadette Dorizzi, London: Springer London, 2009, p. 51-88Chapter in book (Other academic)
Abstract [en]

First, an overview of the state of the art in fingerprint recognition is presented, including current issues and challenges. Fingerprint databases and evaluation campaigns, are also summarized. This is followed by the description of the BioSecure Benchmarking Framework for Fingerprints, using the NIST Fingerpint Image Software (NFIS2), the publicly available MCYT-100 database, and two evaluation protocols. Two research systems are compared within the proposed framework. The evaluated systems follow different approaches for fingerprint processing and are discussed in detail. Fusion experiments involving different combinations of the presented systems are also given. The NFIS2 software is also used to obtain the fingerprint scores for the multimodal experiments conducted within the BioSecure Multimodal Evaluation Campaign(BMEC’2007) reported in Chap.11.

Place, publisher, year, edition, pages
London: Springer London, 2009
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-3312 (URN)10.1007/978-1-84800-292-0_4 (DOI)2-s2.0-84889983758 (Scopus ID)978-1-84800-291-3 (ISBN)978-1-84800-292-0 (ISBN)
Projects
Fingerprints, Fingerprint recognition, Biometric identification, Finger-scan technology
Available from: 2009-12-15 Created: 2009-12-01 Last updated: 2018-03-23Bibliographically approved
Kollreider, K., Fronthaler, H. & Bigun, J. (2009). Non-intrusive liveness detection by face images. Image and Vision Computing, 27(3), 233-244
Open this publication in new window or tab >>Non-intrusive liveness detection by face images
2009 (English)In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 27, no 3, p. 233-244Article in journal (Refereed) Published
Abstract [en]

A technique evaluating liveness in face image sequences is presented. To ensure the actual presence of a live face in contrast to a photograph (playback attack), is a significant problem in face authentication to the extent that anti-spoofing measures are highly desirable. The purpose of the proposed system is to assist in a biometric authentication framework, by adding liveness awareness in a non-intrusive manner. Analyzing the trajectories of certain parts of a live face reveals valuable information to discriminate it against a spoofed one. The proposed system uses a lightweight novel optical flow, which is especially applicable in face motion estimation based on the structure tensor and inputs of a few frames. For reliable face part detection, the system utilizes a model-based local Gabor decomposition and SVM experts, where selected points from a retinotopic grid are used to form regional face models. Also the estimated optical flow is exploited to detect a face part. The whole procedure, starting with three images as input and finishing in a liveness score, is executed in near real-time without special purpose hardware. Experimental results on the proposed system are presented on both a public database and spoofing attack simulations.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2009
Keywords
Face liveness, Liveness detection, Anti-spoofing measures, Optical flow, Motion of lines, Optical flow of lines, Orientation estimation, Face part models, Retinotopic vision, Local Gabor decomposition, Support vector machine classification
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-2180 (URN)10.1016/j.imavis.2007.05.004 (DOI)000262386600003 ()2-s2.0-56349109534 (Scopus ID)2082/2577 (Local ID)2082/2577 (Archive number)2082/2577 (OAI)
Available from: 2008-12-04 Created: 2008-12-04 Last updated: 2018-03-23Bibliographically approved
Fronthaler, H., Kollreider, K., Bigun, J., Fierrez, J., Alonso-Fernandez, F., Ortega-Garcia, J. & Gonzalez-Rodriguez, J. (2008). Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification. IEEE Transactions on Information Forensics and Security, 3(2), 331-338
Open this publication in new window or tab >>Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
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2008 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 3, no 2, p. 331-338Article in journal (Refereed) Published
Abstract [en]

Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. A strongly reduced reference is especially favorable in biometrics, but less information is not sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multialgorithm fingerprint recognition environment. In this study, several trained and nontrained score-level fusion schemes are investigated. A Bayes-based strategy for incorporating experts' past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, is presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training).

Place, publisher, year, edition, pages
New York, N.Y.: IEEE Signal Processing Society, 2008
Keywords
adaptive fusion, Bayesian statistics, cascaded fusion, fingerprint identification, monomodal fusion, quality assessment, structure tensor, symmetry features, Bayes methods, image fusion, image resolution
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-2064 (URN)10.1109/TIFS.2008.920725 (DOI)000257824200017 ()2-s2.0-44049092040 (Scopus ID)2082/2459 (Local ID)2082/2459 (Archive number)2082/2459 (OAI)
Note

©2008 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: 2008-10-18 Created: 2008-10-18 Last updated: 2018-03-23Bibliographically approved
Fronthaler, H., Kollreider, K. & Bigun, J. (2008). Local Features for Enhancement and Minutiae Extraction in Fingerprints. IEEE Transactions on Image Processing, 17(3), 354-363
Open this publication in new window or tab >>Local Features for Enhancement and Minutiae Extraction in Fingerprints
2008 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 17, no 3, p. 354-363Article in journal (Refereed) Published
Abstract [en]

Accurate fingerprint recognition presupposes robust feature extraction which is often hampered by noisy input data. We suggest common techniques for both enhancement and minutiae extraction, employing symmetry features. For enhancement, a Laplacian-like image pyramid is used to decompose the original fingerprint into sub-bands corresponding to different spatial scales. In a further step, contextual smoothing is performed on these pyramid levels, where the corresponding filtering directions stem from the frequency-adapted structure tensor (linear symmetry features). For minutiae extraction, parabolic symmetry is added to the local fingerprint model which allows to accurately detect the position and direction of a minutia simultaneously. Our experiments support the view that using the suggested parabolic symmetry features, the extraction of which does not require explicit thinning or other morphological operations, constitute a robust alternative to conventional minutiae extraction. All necessary image processing is done in the spatial domain using 1-D filters only, avoiding block artifacts that reduce the biometric information. We present comparisons to other studies on enhancement in matching tasks employing the open source matcher from NIST, FIS2. Furthermore, we compare the proposed minutiae extraction method with the corresponding method from the NIST package, mindtct. A top five commercial matcher from FVC2006 is used in enhancement quantification as well. The matching error is lowered significantly when plugging in the suggested methods. The FVC2004 fingerprint database, notable for its exceptionally low-quality fingerprints, is used for all experiments.

Place, publisher, year, edition, pages
New York: IEEE Press, 2008
Keywords
Differential scale space, fidelity, fingerprint restoration, image enhancement, image pyramid, linear symmetry, minutiae extraction, orientation tensor, parabolic symmetry, symmetry features
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-1358 (URN)10.1109/TIP.2007.916155 (DOI)000253272300009 ()2-s2.0-40749102899 (Scopus ID)2082/1737 (Local ID)2082/1737 (Archive number)2082/1737 (OAI)
Note

Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Image Processing. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Halmstad's products or services. Internal or 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 must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Available from: 2008-04-25 Created: 2008-04-25 Last updated: 2018-03-23Bibliographically approved
Kollreider, K., Fronthaler, H. & Bigun, J. (2008). Verifying Liveness by Multiple Experts in Face Biometrics. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops: CVPR 2008, Anchorage, Alaska, June 23-28, 2008. Paper presented at IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, June 23-28, 2008 (pp. 1200-1205). New York: IEEE Press
Open this publication in new window or tab >>Verifying Liveness by Multiple Experts in Face Biometrics
2008 (English)In: IEEE Conference on Computer Vision and Pattern Recognition Workshops: CVPR 2008, Anchorage, Alaska, June 23-28, 2008, New York: IEEE Press, 2008, p. 1200-1205Conference paper, Published paper (Refereed)
Abstract [en]

Resisting spoofing attempts via photographs and video playbacks is a vital issue for the success of face biometrics. Yet, the “liveness ” topic has only been partially studied in the past. In this paper we are suggesting a holistic liveness detection paradigm that collaborates with standard techniques in 2D face biometrics. The experiments show that many attacks are avertible via a combination of antispoofing measures. We have investigated the topic using real-time techniques and applied them to real-life spoofing scenarios in an indoor, yet uncontrolled environment.

Place, publisher, year, edition, pages
New York: IEEE Press, 2008
Series
IEEE Conference on Computer Vision and Pattern Recognition. Proceedings, ISSN 1063-6919
Keywords
Authentication, Biometrics, Collaboration, Collaborative work, Digital cameras, Eyes, Face detection, Face recognition, Hardware, Mouth
National Category
Computer Engineering
Identifiers
urn:nbn:se:hh:diva-14937 (URN)10.1109/CVPRW.2008.4563115 (DOI)000260371900166 ()2-s2.0-51849140964 (Scopus ID)978-1-4244-2339-2 (ISBN)978-142442340-8 (ISBN)
Conference
IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, June 23-28, 2008
Available from: 2011-04-04 Created: 2011-04-04 Last updated: 2018-03-23Bibliographically approved
Alonso-Fernandez, F., Fierrez, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J., Fronthaler, H., Kollreider, K. & Bigun, J. (2007). A Comparative Study of Fingerprint Image-Quality Estimation Methods. IEEE Transactions on Information Forensics and Security, 2(4), 734-743
Open this publication in new window or tab >>A Comparative Study of Fingerprint Image-Quality Estimation Methods
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2007 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 2, no 4, p. 734-743Article in journal (Refereed) Published
Abstract [en]

One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images. In this work, we review existing approaches for fingerprint image-quality estimation, including the rationale behind the published measures and visual examples showing their behavior under different quality conditions. We have also tested a selection of fingerprint image-quality estimation algorithms. For the experiments, we employ the BioSec multimodal baseline corpus, which includes 19 200 fingerprint images from 200 individuals acquired in two sessions with three different sensors. The behavior of the selected quality measures is compared, showing high correlation between them in most cases. The effect of low-quality samples in the verification performance is also studied for a widely available minutiae-based fingerprint matching system.

Place, publisher, year, edition, pages
New York: IEEE Press, 2007
Keywords
Biometrics, fingerprint recognition, minutia, quality assessment
National Category
Engineering and Technology Industrial Biotechnology
Identifiers
urn:nbn:se:hh:diva-2009 (URN)10.1109/TIFS.2007.908228 (DOI)000251110500008 ()2-s2.0-36348952685 (Scopus ID)2082/2404 (Local ID)2082/2404 (Archive number)2082/2404 (OAI)
Note

©2007 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: 2008-10-06 Created: 2008-10-06 Last updated: 2018-03-23Bibliographically approved
Alonso-Fernandez, F., Fierrez-Aguilar, J., Fronthaler, H., Kollreider, K., Ortega-Garcia, J., Gonzalez-Rodriguez, J. & Bigun, J. (2007). Combining multiple matchers for fingerprint verification: A case study in biosecure network of excellence. Annales des télécommunications, 62(1-2), 62-82
Open this publication in new window or tab >>Combining multiple matchers for fingerprint verification: A case study in biosecure network of excellence
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2007 (English)In: Annales des télécommunications, ISSN 0003-4347, E-ISSN 1958-9395, Vol. 62, no 1-2, p. 62-82Article in journal (Refereed) Published
Abstract [en]

We report on experiments for the fingerprint modality conducted during the First BioSecure Residential Workshop. Two reference systems for fingerprint verification have been tested together with two additional non-reference systems. These systems follow different approaches of fingerprint processing and are discussed in detail. Fusion experiments involving different combinations of the available systems are presented. The experimental results show that the best recognition strategy involves both minutiae-based and correlation-based measurements. Regarding the fusion experiments, the best relative improvement is obtained when fusing systems that are based on heterogeneous strategies for feature extraction and/or matching. The best combinations of two/three/four systems always include the best individual systems whereas the best verification performance is obtained when combining all the available systems.

Place, publisher, year, edition, pages
Paris, France: Springer, 2007
Keywords
Biometrics, Pattern recognition, Fingerprint, Comparative study, Case study, Identification, Research program, Experimental study, Performance evaluation, Mixed method, Data fusion
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:hh:diva-2070 (URN)000245366300004 ()2-s2.0-34247095670 (Scopus ID)2082/2465 (Local ID)2082/2465 (Archive number)2082/2465 (OAI)
Available from: 2008-10-20 Created: 2008-10-20 Last updated: 2018-03-23Bibliographically approved
Fronthaler, H., Kollreider, K. & Bigun, J. (2007). Pyramid-based Image Enhancement of Fingerprints. In: 2007 IEEE Workshop on Automatic Identification Advanced Technologies proceedings : 7-8 June 2007, Alghero, Italy. Paper presented at 2007 IEEE Workshop on Automatic Identification Advanced Technologies proceedings : 7-8 June 2007, Alghero, Italy (pp. 45-50). Piscataway, NJ.: IEEE Press
Open this publication in new window or tab >>Pyramid-based Image Enhancement of Fingerprints
2007 (English)In: 2007 IEEE Workshop on Automatic Identification Advanced Technologies proceedings : 7-8 June 2007, Alghero, Italy, Piscataway, NJ.: IEEE Press, 2007, p. 45-50Conference paper, Published paper (Refereed)
Abstract [en]

Reliable feature extraction is crucial for accurate biometric recognition. Unfortunately feature extraction is hampered by noisy input data, especially so in case of fingerprints. We propose a method to enhance the quality of a given fingerprint with the purpose to improve the recognition performance. A Laplacian like image-scale pyramid is used for this purpose to decompose the original fingerprint into 3 smaller images corresponding to different frequency bands. In a further step, contextual filtering is performed using these pyramid levels and 1D Gaussians, where the corresponding filtering directions are derived from the frequency-adapted structure tensor. All image processing is done in the spatial domain, avoiding block artifacts while conserving the biometric signal well. We report on comparative results and present quantitative improvements, by applying the standardized NIST FIS2 fingerprint matcher to the FVC2004 fingerprint database along with our as well as two other enhancements. The study confirms that the suggested enhancement robustifies feature detection, e.g. minutiae, which in turn improves the recognition (20% relative improvement in equal error rate on DB3 of FVC2004).

Place, publisher, year, edition, pages
Piscataway, NJ.: IEEE Press, 2007
Keywords
Gaussian processes, feature extraction, filtering theory, fingerprint identification, image enhancement, image matching, image segmentation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-2131 (URN)10.1109/AUTOID.2007.380591 (DOI)000247964900009 ()2-s2.0-34748815133 (Scopus ID)2082/2526 (Local ID)1-4244-1300-1 (ISBN)2082/2526 (Archive number)2082/2526 (OAI)
Conference
2007 IEEE Workshop on Automatic Identification Advanced Technologies proceedings : 7-8 June 2007, Alghero, Italy
Note

©2007 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: 2008-11-12 Created: 2008-11-12 Last updated: 2018-03-23Bibliographically approved
Kollreider, K., Fronthaler, H., Faraj, M. & Bigun, J. (2007). Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment. IEEE Transactions on Information Forensics and Security, 2(3 part 2), 548-558
Open this publication in new window or tab >>Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment
2007 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 2, no 3 part 2, p. 548-558Article in journal (Refereed) Published
Abstract [en]

A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers.

Place, publisher, year, edition, pages
New York: IEEE Press, 2007
Keywords
AdaBoost, antispoofing, face detection, landmark detection, lip reading, liveness, object detection, optical flow of lines, quantized angles, real-time processing, support vector machine, SVM
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-2021 (URN)10.1109/TIFS.2007.902037 (DOI)000248832500007 ()2-s2.0-34548094310 (Scopus ID)2082/2416 (Local ID)2082/2416 (Archive number)2082/2416 (OAI)
Note

©2007 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: 2008-10-06 Created: 2008-10-06 Last updated: 2018-03-23Bibliographically approved
Kollreider, K., Fronthaler, H. & Bigun, J. (2007). Real-Time Face Detection Using Illumination Invariant Features. In: Ersboll, B K, Pedersen, K S (Ed.), Image Analysis: Proceedings. Paper presented at 15th Scandinavian Conference on Image Analysis, Aalborg, Denmark, June 10-14, 2007 (pp. 41-50). Berlin: Springer
Open this publication in new window or tab >>Real-Time Face Detection Using Illumination Invariant Features
2007 (English)In: Image Analysis: Proceedings / [ed] Ersboll, B K, Pedersen, K S, Berlin: Springer, 2007, p. 41-50Conference paper, Published paper (Refereed)
Abstract [en]

A robust object/face detection technique processing every frame in real-time (video-rate) is presented. A methodological novelty are the suggested quantized angle features (“quangles”), being designed for illumination invariance without the need for pre-processing, e.g. histogram equalization. This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. Separable filtering and the use of lookup tables favor the detection speed. Furthermore, the gradient may then be reused for other tasks as well. A side effect is that the training of effective cascaded classifiers is feasible in very short time, less than 1 hour for data sets of order 104. We present favorable results on face detection, for several public databases (e.g. 93% Detection Rate at 1×10− 6 False Positive Rate on the CMU-MIT frontal face test set).

Place, publisher, year, edition, pages
Berlin: Springer, 2007
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; Volume 4522/2007
Keywords
Object detection, Image analysis, Biometrics, Direction field, Orientation tensor, Quantized angles, Quangles, AdaBoost
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-2067 (URN)10.1007/978-3-540-73040-8_5 (DOI)000247364000005 ()2-s2.0-38049033993 (Scopus ID)2082/2462 (Local ID)978-3-540-73039-2 (ISBN)2082/2462 (Archive number)2082/2462 (OAI)
Conference
15th Scandinavian Conference on Image Analysis, Aalborg, Denmark, June 10-14, 2007
Available from: 2008-10-20 Created: 2008-10-20 Last updated: 2018-03-23Bibliographically approved
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