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Impact of Neural Network Architecture for Fingerprint Recognition

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Intelligent Systems and Pattern Recognition (ISPR 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1940))

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Abstract

This work investigates the impact of the neural networks architecture when performing fingerprint recognition. Three networks are studied; a Triplet network and two Siamese networks. They are evaluated on datasets with specified amounts of relative translation between fingerprints. The results show that the Siamese model based on contrastive loss performed best in all evaluated metrics. Moreover, the results indicate that the network with a categorical scheme performed inferior to the other models, especially in recognizing images with high confidence. The Equal Error Rate (EER) of the best model ranged between \(4\% - 11 \%\) which was on average 6.5 percentage points lower than the categorical schemed model. When increasing the translation between images, the networks were predominantly affected once the translation reached a fourth of the image. Our work concludes that architectures designed to cluster data have an advantage when designing an authentication system based on neural networks.

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Authors and Affiliations

  1. Blekinge Institute of Technology, Biblioteksgatan 8, 374 32, Karlshamn, Blekinge, Sweden

    Simon Hallösta, Mats I. Pettersson & Mattias Dahl

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  1. Simon Hallösta
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  2. Mats I. Pettersson
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  3. Mattias Dahl
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Correspondence to Simon Hallösta .

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Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Akram Bennour

  2. Sharjah University, Sharjah, United Arab Emirates

    Ahmed Bouridane

  3. University of Toulouse, Toulouse, France

    Lotfi Chaari

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Hallösta, S., Pettersson, M.I., Dahl, M. (2024). Impact of Neural Network Architecture for Fingerprint Recognition. In: Bennour, A., Bouridane, A., Chaari, L. (eds) Intelligent Systems and Pattern Recognition. ISPR 2023. Communications in Computer and Information Science, vol 1940. Springer, Cham. https://doi.org/10.1007/978-3-031-46335-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-46335-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46334-1

  • Online ISBN: 978-3-031-46335-8

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