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International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences

MCO 2021: Modelling, Computation and Optimization in Information Systems and Management Sciences pp 327–337Cite as

Training Deep Network Models for Fingerprint Image Classification

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 363))

Abstract

Our investigation aims to answer the research question is it possible to train deep network models that can be re-used to classify a new coming dataset of fingerprint images without re-training the new deep network model? For this purpose, we collect real datasets of fingerprint images from students at the Can Tho University. After that, we propose to train recent deep networks, such as VGG, ResNet50, Inception-v3, Xception, on the training dataset with 9,236 fingerprint images of 441 students, to create deep network models. And then, we re-use these resulting deep network models as the feature extraction and only fine-tune the last layer in deep network models for the new fingerprint image datasets. The empirical test results on three real fingerprint image datasets (FP-235, FP-389, FP-559) show that deep network models achieve at least the accuracy of 96.72% on the testsets. Typically, the ResNet50 models give classification accuracy of 99.00%, 98.33%, 98.05% on FP-235, FP-389 and FP-559, respectively queryAs per Springer style, both city and country names must be present in the affiliations. Accordingly, we have inserted the city name “Paris” in affiliation 2. Please check and confirm if the inserted city name is correct. If not, please provide us with the correct city name..

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Acknowledgments

This work has received support from the College of Information Technology, Can Tho University. The author would like to thank very much the Big Data and Mobile Computing Laboratory.

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Authors and Affiliations

  1. College of Information Technology, Can Tho University, 92000, Cantho, Vietnam

    Thanh-Nghi Do & Minh-Thu Tran-Nguyen

  2. UMI UMMISCO 209 (IRD/UPMC), Sorbonne University, Pierre and Marie Curie University - Paris 6, Paris, France

    Thanh-Nghi Do

Authors
  1. Thanh-Nghi Do
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  2. Minh-Thu Tran-Nguyen
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Corresponding author

Correspondence to Thanh-Nghi Do .

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Editors and Affiliations

  1. Computer science and Applications Department, LGIPM, University of Lorraine, Metz Cedex, France

    Hoai An Le Thi

  2. Laboratory of Mathematics, National Institute for Applied Sciences - Rouen, Saint-Etienne-du-Rouvray Cedex, France

    Tao Pham Dinh

  3. Computer science and Applications Department, LGIPM, University of Lorraine, Metz Cedex, France

    Hoai Minh Le

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Do, TN., Tran-Nguyen, MT. (2022). Training Deep Network Models for Fingerprint Image Classification. In: Le Thi, H.A., Pham Dinh, T., Le, H.M. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2021. Lecture Notes in Networks and Systems, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-92666-3_28

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