Abstract
Minutiae based matching, the most popular approach used in fingerprint matching algorithms, is to calculate the similarity by finding the maximum number of matched minutiae pairs in two given fingerprints. With no prior knowledge about anchor/clue to match, this becomes a combinatorial problem. Global features of the fingerprints (e.g., singular core and delta points) can be used as the anchor to speed up the matching process. Most approaches use the conventional Poincare Index method with additional techniques to improve the detection of the core and delta points. Our approach uses Convolution Neural Networks which gained state-of-the-art results in many computer vision tasks to automatically detect those points. With the experimental results on FVC2002 database, we achieved the accuracy and false alarm of (96%, 7.5%) and (90%, 6%) for detecting core, and delta points, correspondingly. These results are comparative to those of the detection algorithms with human knowledge.
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Le, H.H., Nguyen, N.H., Nguyen, TT. (2017). Automatic Detection of Singular Points in Fingerprint Images Using Convolution Neural Networks. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_20
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DOI: https://doi.org/10.1007/978-3-319-54430-4_20
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