ConvNet Regression for Fingerprint Orientations View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Patrick Schuch , Simon-Daniel Schulz , Christoph Busch

ABSTRACT

Estimation of orientation fields is a crucial task in fingerprint recognition. Many processing steps depend on their precise estimation and the direction of fingerprint minutiae is a valuable information. But especially for regions of low quality the task is not trivial and engineered approaches on local features may fail. Methods that combine local and global features learned from the data are state of the art and benchmarked with the framework FVC-ongoing. We propose to use Convolutional Neural Networks trained in a regression to estimate the orientation field (ConvNetOF). Regression is more accurate than classification in this case. Our approach achieves an RMSE of 8.53\(^{\circ }\) on the Bad Quality Dataset of the FVC-ongoing benchmark. This is the best result reported so far. More... »

PAGES

325-336

References to SciGraph publications

Book

TITLE

Image Analysis

ISBN

978-3-319-59125-4
978-3-319-59126-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-59126-1_27

DOI

http://dx.doi.org/10.1007/978-3-319-59126-1_27

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1086114495


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