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P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group

https://dl.gi.de/handle/20.500.12116/23780
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Auflistung P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group nach Schlagwort "Biometric recognition"

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  • Konferenzbeitrag
    PROTECT Multimodal DB: fusion evaluation on a novel multimodal biometrics dataset envisaging Border Control
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Sequeira, Ana F.; Chen, Lulu; Ferryma, James; Galdi, Chiara; Chiesa, Valeria; Dugelay, Jean-Luc; Maik, Patryk; Gmitrowicz, Piotr; Szklarski, Lukasz; Prommegger, Bernhard; Kauba, Christof; Kirchgasser, Simon; Uhl, Andreas; Grudzien, Artur; Kowalski, Marcin
    This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible iris, finger and hand veins, voice and anthropometrics. This dataset will constitute a valuable resource to the field with its number and variety of biometric traits. Acquired in the context of the EU PROTECT project, the dataset allows several combinations of biometric traits and envisages applications such as border control. Based upon the results of the unimodal data, a fusion scheme was applied to ascertain the recognition potential of combining these biometric traits in a multimodal approach. Due to the variability on the discriminative power of the traits, a leave the n-best out fusion technique was applied to obtain different recognition results.
  • Konferenzbeitrag
    Robust Clustering-based Segmentation Methods for Fingerprint Recognition
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Ferreira, Pedro M.; Sequeira, Ana F.; Cardoso, Jaime S.; Rebelo, Ana
    Fingerprint recognition has been widely studied for more than 45 years and yet it remains an intriguing pattern recognition problem. This paper focuses on the foreground mask estimation which is crucial for the accuracy of a fingerprint recognition system. The method consists of a robust cluster-based fingerprint segmentation framework incorporating an additional step to deal with pixels that were rejected as foreground in a decision considered not reliable enough. These rejected pixels are then further analysed for a more accurate classification. The procedure falls in the paradigm of classification with reject option - a viable option in several real world applications of machine learning and pattern recognition, where the cost of misclassifying observations is high. The present work expands a previous method based on the fuzzy C-means clustering with two variations regarding: i) the filters used; and ii) the clustering method for pixel classification as foreground/background. Experimental results demonstrate improved results on FVC datasets comparing with state-of-the-art methods even including methodologies based on deep learning architectures.