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Auflistung nach Schlagwort "fingerprint recognition"

1 - 4 von 4
  • Textdokument
    Benchmarking Fingerprint Minutiae Extractors
    (BIOSIG 2017, 2017) Chugh,Tarang; Arora,Sunpreet S.; Jain, Anil K.; Paulter Jr.,Nicholas G.
    The performance of a fingerprint recognition system hinges on the errors introduced in each of its modules: image acquisition, preprocessing, feature extraction, and matching. One of the most critical and fundamental steps in fingerprint recognition is robust and accurate minutiae extraction. Hence we conduct a repeatable and controlled evaluation of one open-source and three commercial-off-the-shelf (COTS) minutiae extractors in terms of their performance in minutiae detection and localization. We also evaluate their robustness against controlled levels of image degradations introduced in the fingerprint images. Experiments were conducted on (i) a total of 3;458 fingerprint images from five public-domain databases, and (ii) 40;000 synthetically generated fingerprint images. The contributions of this study include: (i) a benchmark for minutiae extractors and minutiae interoperability, and (ii) robustness of minutiae extractors against image degradations.
  • Konferenzbeitrag
    Estimating the Data Origin of Fingerprint Samples
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Schuch, Patrick; May, Jan Marek; Busch, Christoph
    The data origin (i.e. acquisition technique and acquisition mode) can have a significant impact on the appearance and characteristics of a fingerprint sample. This dataset bias might be challenging for processes like biometric feature extraction. Much effort can be put into data normalization or into processes able to deal with almost any input data. The performance of the former might suffer from this general applicability. The latter losses information by definition. If one is able to reliably identify the data origin of fingerprints, one will be able to dispatch the samples to specialized processes. Six methods of classification are evaluated for their capabilities to distinguish between fifteen different datasets. Acquisition technique and acquisition mode can be classified very accurately. Also, most of the datasets can be distinguished reliably.
  • Textdokument
    Intrinsic Limitations of Fingerprint Orientation Estimation
    (BIOSIG 2017, 2017) Schuch,Patrick; Schulz,Simon-Daniel; Busch,Christoph
    Estimation of orientation field is a crucial issue when processing fingerprint samples. Many subsequent fingerprint processing steps depend on reliable and accurate estimations. Algorithms for such estimations are usually evaluated against ground truth data. As true ground truth is usually not available, human experts need to mark-up ground truth manually. However, the accuracy and the reliability of such mark-ups for orientation fields have not been investigated yet. Mark-ups produced by six humans allowed insights into both aspects. A Root Mean Squared Error of about 7 against true ground truth can be achieved. Reproducibility between two mark-ups of a single dactyloscopic expert is at the same precision. We concluded that the accuracy of human experts is competitive to the best algorithms evaluated at FVC-ongoing.
  • Konferenzbeitrag
    Unsupervised Learning of Fingerprint Rotations
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Schuch, Patrick; May, Jan Marek; Busch, Christoph
    The alignment of fingerprint samples is a preprocessing step in fingerprint recognition. It allows an improved biometric feature extraction and a more accurate biometric comparison. We propose to use Convolutional Neural Networks for estimation of the rotational part. The main contribution is an unsupervised training strategy similar to Siamese Networks for estimation of rotations. The approach does not need any labelled data for training. It is trained to estimate orientation differences for pairs of samples. Our approach achieves an alignment accuracy with a mean absolute deviation 2:1 on data similar to the training data, which supports the alignment task. For other datasets accuracies down to 6:2 mean absolute deviation are achieved.