Gottschlich, Carsten

[["dc.bibliographiccitation.firstpage","1007"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society. Series C, Applied Statistics"],["dc.bibliographiccitation.lastpage","1027"],["dc.bibliographiccitation.volume","68"],["dc.contributor.author","Markert, Karla"],["dc.contributor.author","Krehl, Karolin"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2020-12-10T18:36:28Z"],["dc.date.available","2020-12-10T18:36:28Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1111/rssc.12343"],["dc.identifier.issn","0035-9254"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76633"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","DOI-Import GROB-394"],["dc.relation","RTG 2088: Research Training Group 2088 Discovering structure in complex data: Statistics meets Optimization and Inverse Problems"],["dc.title","Detecting anisotropy in fingerprint growth"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","78"],["dc.bibliographiccitation.lastpage","82"],["dc.contributor.author","Imdahl, Christina"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.editor","Lončarić, S."],["dc.date.accessioned","2017-09-07T11:48:35Z"],["dc.date.available","2017-09-07T11:48:35Z"],["dc.date.issued","2015"],["dc.description.abstract","Synthetic fingerprint generation has two major advantages. First, it is possible to create arbitrarily large databases for research purposes e.g. of a million or a billion fingerprints at virtually no cost and without legal constraints. Secondly, together with the generated fingerprint images comes additional ground truth information for free such as e.g. the corresponding minutiae template. However, recently it has been shown that existing methods in the literature synthesize images with unrealistic minutiae configurations, usually not visible to the naked eye of an expert. In this paper, we propose an algorithm called Realistic Fingerprint Creator (RFC) for the generation realistic synthetic fingerprint images, which, as a core ingredient, involves a selection procedure how to choose the most 'realistic' synthetic fingerprints to build a database. We have performed a test of realness comparing prints synthesized by RFC and real fingerprints, and we have observed that the proposed RFC is the first method which produces artificial fingerprints that pass this test due to their realistic minutiae configuration."],["dc.identifier.doi","10.1109/ispa.2015.7306036"],["dc.identifier.gro","3146930"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4712"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.publisher","IEEE"],["dc.publisher.place","Piscataway"],["dc.relation.conference","9th International Symposium on Image and Signal Processing and Analysis"],["dc.relation.eventend","2015-09-09"],["dc.relation.eventlocation","Zagreb"],["dc.relation.eventstart","2015-09-07"],["dc.relation.isbn","978-1-4673-8032-4"],["dc.relation.ispartof","ISPA 2015 : 9th International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia, September 7-9, 2015"],["dc.title","Towards generating realistic synthetic fingerprint images"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","802"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Information Forensics and Security"],["dc.bibliographiccitation.lastpage","811"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Mihailescu, Preda"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2017-09-07T11:46:45Z"],["dc.date.available","2017-09-07T11:46:45Z"],["dc.date.issued","2009"],["dc.description.abstract","Orientation field ( OF) estimation is a crucial preprocessing step in fingerprint image processing. In this paper, we present a novel method for OF estimation that uses traced ridge and valley lines. This approach provides robustness against disturbances caused, e. g., by scars, contamination, moisture, or dryness of the finger. It considers pieces of flow information from a larger region and makes good use of fingerprint inherent properties like continuity of ridge flow perpendicular to the flow. The performance of the line-sensor method is compared with the gradients-based method and a multiscale directional operator. Its robustness is tested in experiments with simulated scar noise which is drawn on top of good quality fingerprint images from the FVC2000 and FVC2002 databases. Finally, the effectiveness of the line-sensor-based approach is demonstrated on 60 naturally poor quality fingerprint images from the FVC2004 database. All orientations marked by a human expert are made available at the journal's and the authors' website for comparative tests."],["dc.identifier.doi","10.1109/TIFS.2009.2033219"],["dc.identifier.gro","3143017"],["dc.identifier.isi","000271968200005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/485"],["dc.notes.intern","WoS Import 2017-03-10 / Funder: DFG [FOR 916]; Volkswagen Foundation"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1556-6013"],["dc.title","Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2220"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Image Processing"],["dc.bibliographiccitation.lastpage","2227"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Gottschlich, Carsten"],["dc.date.accessioned","2018-11-07T09:11:40Z"],["dc.date.available","2018-11-07T09:11:40Z"],["dc.date.issued","2012"],["dc.description.abstract","Gabor filters (GFs) play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved GFs that locally adapt their shape to the direction of flow. These curved GFs enable the choice of filter parameters that increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved GFs are applied to the curved ridge and valley structures of low-quality fingerprint images. First, we combine two orientation-field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation. Subsequently, these curved regions are used for estimating the local ridge frequency. Finally, curved GFs are defined based on curved regions, and they apply the previously estimated orientations and ridge frequencies for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison with state-of-the-art enhancement methods."],["dc.description.sponsorship","DFG RTG [1023]"],["dc.identifier.doi","10.1109/TIP.2011.2170696"],["dc.identifier.isi","000302181800064"],["dc.identifier.pmid","21984503"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/26774"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1941-0042"],["dc.relation.issn","1057-7149"],["dc.title","Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.lastpage","6"],["dc.contributor.author","Oehlmann, Lars"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Gottschlich, Carsten"],["dc.date.accessioned","2017-09-07T11:48:36Z"],["dc.date.available","2017-09-07T11:48:36Z"],["dc.date.issued","2015"],["dc.description.abstract","Orientation fields (OFs) are a key element of fingerprint recognition systems. They are a requirement for important processing steps such as image enhancement by contextual filtering, and typically, they are estimated from fingerprint images. If information about a fingerprint is available only in form of a stored minutiae template, an OF can be reconstructed from this template up to a certain degree of accuracy. The reconstructed OF can then be used e.g. for fingerprint alignment or as a feature for matching, and thus, for improving directly or indirectly the recognition performance of a system. This study compares reconstruction methods from the literature on a benchmark with ground truth orientation fields. The performance of these methods is evaluated using three metrics measuring the amount of reconstruction errors as well as in terms of computational runtime."],["dc.identifier.doi","10.1109/iwbf.2015.7110237"],["dc.identifier.gro","3146931"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4713"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","IEEE"],["dc.publisher.place","Piscataway, NJ"],["dc.relation.conference","International Workshop on Biometrics and Forensics"],["dc.relation.eventend","2015-03-05"],["dc.relation.eventlocation","Gjøvik, Norway"],["dc.relation.eventstart","2015-03-03"],["dc.relation.isbn","978-1-4799-8105-2"],["dc.relation.ispartof","3rd International Workshop on Biometrics and Forensics (IWBF2015)"],["dc.title","Performance evaluation of fingerprint orientation field reconstruction methods"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","183"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","IET Biometrics"],["dc.bibliographiccitation.lastpage","190"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Tams, Benjamin"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2017-09-07T11:48:37Z"],["dc.date.available","2017-09-07T11:48:37Z"],["dc.date.issued","2016"],["dc.description.abstract","Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this study, a novel approach is presented to globally model an OF combined with locally adaptive methods. The authors show that this model adapts perfectly to the ‘true OF’ in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high-fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution, they describe an algorithm to perfectly estimate OF parameters automatically or semi-automatically, depending on image quality, and they establish the main underlying claim of high-fidelity low parameter OF compression."],["dc.identifier.doi","10.1049/iet-bmt.2016.0087"],["dc.identifier.gro","3146922"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4708"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","2047-4938"],["dc.title","Perfect fingerprint orientation fields by locally adaptive global models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","11"],["dc.bibliographiccitation.lastpage","20"],["dc.contributor.author","Hotz, Thomas"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Lorenz, Robert"],["dc.contributor.author","Bernhardt, Stefanie"],["dc.contributor.author","Hantschel, Michael"],["dc.contributor.author","Munk, Axel"],["dc.contributor.editor","Brömme, Arslan"],["dc.contributor.editor","Busch, Christoph"],["dc.date.accessioned","2018-02-05T09:43:18Z"],["dc.date.available","2018-02-05T09:43:18Z"],["dc.date.issued","2011"],["dc.description.abstract","We study the effect of growth on the fingerprints of adolescents, based on which we suggest a simple method to adjust for growth when trying to retrieve an adolescent's fingerprint in a database years later. Here, we focus on the statistical analyses used to determine how fingerprints grow: Procrustes analysis allows us to establish that fingerprints grow isotropically, an appropriate mixed effects model shows that fingerprints essentially grow proportionally to body height. The resulting growth model is validated by showing that it brings points of interest as close as if both fingerprints were taken from an adult. Further details on this study, in particular results when applying our growth model in verification and identification tests, can be found in C. Gottschlich, T. Hotz, R. Lorenz, S. Bernhardt, M. Hantschel and A. Munk: Modeling the Growth of Fingerprints Improves Matching for Adolescents, IEEE Transations on Information Forensics and Security, 2011"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/11949"],["dc.language.iso","en"],["dc.notes.status","fcwi-famis"],["dc.publisher","Gesellschaft für Informatik"],["dc.publisher.place","Bonn"],["dc.relation.eventend","09.09.2011"],["dc.relation.eventlocation","Darmstadt"],["dc.relation.eventstart","08.09.2011"],["dc.relation.ispartof","BIOSIG 2011 Proceedings - international conference of the biometrics special interest group"],["dc.title","Statistical analyses of fingerprint growth"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","120"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","IET Biometrics"],["dc.bibliographiccitation.lastpage","130"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Thai, Duy Hoang"],["dc.contributor.author","Gottschlich, Carsten"],["dc.date.accessioned","2018-11-07T10:13:39Z"],["dc.date.available","2018-11-07T10:13:39Z"],["dc.date.issued","2016"],["dc.description.abstract","Verifying an identity claim by fingerprint recognition is a commonplace experience for millions of people in their daily life, for example, for unlocking a tablet computer or smartphone. The first processing step after fingerprint image acquisition is segmentation, that is, dividing a fingerprint image into a foreground region which contains the relevant features for the comparison algorithm, and a background region. The authors propose a novel segmentation method by global three-part decomposition (G3PD). On the basis of global variational analysis, the G3PD method decomposes a fingerprint image into cartoon, texture and noise parts. After decomposition, the foreground region is obtained from the non-zero coefficients in the texture image using morphological processing. The segmentation performance of the G3PD method is compared with five state-of-the-art methods on a benchmark which comprises manually marked ground truth segmentation for 10,560 images. Performance evaluations show that the G3PD method consistently outperforms existing methods in terms of segmentation accuracy."],["dc.identifier.doi","10.1049/iet-bmt.2015.0010"],["dc.identifier.isi","000376976100009"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/40475"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Inst Engineering Technology-iet"],["dc.relation.issn","2047-4946"],["dc.relation.issn","2047-4938"],["dc.title","Global variational method for fingerprint segmentation by three-part decomposition"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","291"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IET Biometrics"],["dc.bibliographiccitation.lastpage","301"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2017-09-07T11:50:30Z"],["dc.date.available","2017-09-07T11:50:30Z"],["dc.date.issued","2014"],["dc.description.abstract","In this study, the authors show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. They propose a non-parametric distribution-based method using second-order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This ‘test of realness’ can be applied to synthetic fingerprints produced by any method. In this study, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms; 3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. In addition, they evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz against real fingerprint images. They conclude with suggestions for the improvement of synthetic fingerprint generation."],["dc.identifier.doi","10.1049/iet-bmt.2013.0065"],["dc.identifier.gro","3147637"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5095"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","2047-4938"],["dc.title","Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","529"],["dc.bibliographiccitation.lastpage","533"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Mihailescu, Preda"],["dc.contributor.editor","Zinterhof, Peter"],["dc.date.accessioned","2017-09-07T11:53:17Z"],["dc.date.available","2017-09-07T11:53:17Z"],["dc.date.issued","2009"],["dc.description.abstract","A good and reliable orientation field (OF) estimation is of great importance in fingerprint image processing. This paper presents an OF estimation approach that is based on traced ridge and furrow lines. The method considers pieces of flow information from a larger region and provides robustness against disturbances caused, e.g. by temporary scars, creases, contamination, wetness or dryness of the finger. The performance of the method is evaluated in simulation runs with artificial ridge interruptions and compared to the gradients based method and a multiscale directional operator."],["dc.identifier.doi","10.1109/ispa.2009.5297684"],["dc.identifier.gro","3145067"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/2762"],["dc.language.iso","en"],["dc.notes.intern","Crossref Import"],["dc.notes.status","final"],["dc.publisher","IEEE"],["dc.publisher.place","Piscataway"],["dc.relation.eventend","2009-09-18"],["dc.relation.eventlocation","Salzburg"],["dc.relation.eventstart","2009-09-16"],["dc.relation.isbn","978-953-184-135-1"],["dc.relation.ispartof","Proceedings of the 6th International Symposium on Image and Signal Processing and Analysis, 2009 : ISPA 2009"],["dc.title","Robust orientation field estimation in fingerprint images with broken ridge lines"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]