Robust Clustering-based Segmentation Methods for Fingerprint Recognition

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Pedro M. Ferreira (Author) Other; Ana F. Sequeira (Author) Other; Jaime S. Cardoso (Author) FEUP; Ana Rebelo (Author) Other

Conference proceedings International

Title: 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018

Pages: 1-5

2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018

26 September 2018 through 28 September 2018

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ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

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Other information

Authenticus ID: P-00R-R6N

DOI: 10.23919/biosig.2018.8553022

Abstract (EN): 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. © 2018 Gesellschaft fuer Informatik.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5