Title: Novel mobile palmprint databases for biometric authentication

Authors: Mahdieh Izadpanahkakhk; Seyyed Mohammad Razavi; Mehran Taghipour-Gorjikolaie; Seyyed Hamid Zahiri; Aurelio Uncini

Addresses: Department of Electrical and Computer Engineering, University of Birjand, Birjand, Southern Khorasan, Iran ' Department of Electrical and Computer Engineering, University of Birjand, Birjand, Southern Khorasan, Iran ' Department of Electrical and Computer Engineering, University of Birjand, Birjand, Southern Khorasan, Iran ' Department of Electrical and Computer Engineering, University of Birjand, Birjand, Southern Khorasan, Iran ' Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00185 Rome, Italy

Abstract: Mobile palmprint biometric authentication has attracted a lot of attention as an interesting analytics tool for representing discriminative features. Despite the advances in this technology, there are some challenges including lack of enough data and invariant templates to the rotation, illumination, and translation. In this paper, we provide two mobile palmprint databases and we can address the aforementioned challenges via deep convolutional neural networks. In the best of our knowledge, this paper is the first study in which mobile palmprint images were acquired in some special views and then were evaluated via deep learning training algorithms. To evaluate our mobile palmprint images, some well-known convolutional neural networks are applied for verification task. By using these networks, the best performing results are achieved via GoogLeNet and CNN-F architectures in terms of cost of the training phase and classification accuracy of the test phase obtained in the 1-to-1 matching procedure.

Keywords: training algorithms; biometric authentication; palmprint verification; mobile devices; deep learning; convolutional neural network; feature extraction.

DOI: 10.1504/IJGUC.2019.102016

International Journal of Grid and Utility Computing, 2019 Vol.10 No.5, pp.465 - 474

Received: 18 Oct 2018
Accepted: 19 Nov 2018
Published online: 03 Sep 2019 *

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