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%.
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Shehu, Y.I., Ruiz-Garcia, A., Palade, V., James, A. (2018). Detection of Fingerprint Alterations Using Deep Convolutional Neural Networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_6
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