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A Fast and Light Fingerprint-Matching Model Based on Deep Learning Approaches

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Abstract

Nowadays, biometric identification has become very important due to the need to identify people in different places and devices. Among these features, the fingerprint has received more attention than others because of its biometric criteria and the ability to use easily and quickly. Neural network-based methods received considerable attention due to their high accuracy and performance. These methods also do not need data preprocessing and image segmentation. In identification systems, hardware implementation capability is critical. This paper proposes a novel convolutional neural network architecture for identification using fingerprints. The proposed architecture in this paper offers more than 94% accuracy using different databases. Also, by reducing the number of parameters and memory used by more than 75% compared to state-of-the-art counterparts and the number of convolutional layers, the proposed architecture is hardware friendly and offers at least 10% better speed than the state-of-the-art counterparts.

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Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

The authors did not receive support from any organization for the submitted work. The authors declare they have no financial interests.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

    Hamid Shafaghi, Meysam Kiani, Abdolah Amirany, Kian Jafari & Mohammad Hossein Moaiyeri

  2. Department of Electrical and Computer Engineering (ECE), University of Toronto, Toronto, Canada

    Kian Jafari

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  1. Hamid Shafaghi
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Correspondence to Kian Jafari.

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Shafaghi, H., Kiani, M., Amirany, A. et al. A Fast and Light Fingerprint-Matching Model Based on Deep Learning Approaches. J Sign Process Syst 95, 551–558 (2023). https://doi.org/10.1007/s11265-023-01870-y

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