Detection of fingerprint alterations using deep convolutional neural networks

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Shehu, Y.I., Ruiz-Garcia, A., Palade, V. and James, A. ORCID: 0000-0001-9274-7803, 2018. Detection of fingerprint alterations using deep convolutional neural networks. In: V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis and I. Maglogiannis, eds., Artificial neural networks and machine learning – ICANN 2018. Proceedings of the 27th International Conference on Artificial Neural Networks, Rhodes, Greece, 4-7 October 2018, Part 1. Lecture notes in computer science (11139). Cham: Springer, pp. 51-60. ISBN 9783030014179

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Official URL: http://doi.org/10.1007/978-3-030-01418-6_6

Abstract

Fingerprint alteration is a challenge that poses enormous security risks. As a result, many research efforts in the scientific community have attempted to address this issue. However, non-existence of publicly available datasets that contain obfuscation and distortion of fingerprints makes it difficult to identify the type of alteration. In this work we present the publicly available Sokoto-Coventry Fingerprints Dataset (SOCOFing), which provides ten fingerprints for 600 different subjects, as well as gender, hand and finger name for each image, among other unique characteristics. We also provide a total of 55,249 images with three levels of alteration for Z-cut, obliteration and central rotation synthetic alterations, which are the most common types of obfuscation and distortion. In addition, this paper proposes a Convolutional Neural Network (CNN) to identify these alterations. The proposed CNN model achieves a classification accuracy rate of 98.55%. Results are also compared with a residual CNN model pre-trained on ImageNet, which produces an accuracy of 99.88%.

Item Type:

Chapter in book

Creators:

Shehu, Y.I., Ruiz-Garcia, A., Palade, V. and James, A.

Publisher:

Springer

Place of Publication:

Cham

Date:

2018

Number:

11139