TY - GEN
T1 - Detection of fingerprint alterations using deep convolutional neural networks
AU - Shehu, Yahaya Isah
AU - Ruiz-Garcia, Ariel
AU - Palade, Vasile
AU - James, Anne
PY - 2018/9/27
Y1 - 2018/9/27
N2 - 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%.
AB - 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%.
KW - Central rotation
KW - Convolutional neural networks
KW - Distortion
KW - Fingerprint alteration
KW - Obfuscation
KW - Obliteration
KW - Z-cut
UR - http://www.scopus.com/inward/record.url?scp=85054781898&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01418-6_6
DO - 10.1007/978-3-030-01418-6_6
M3 - Conference proceeding
AN - SCOPUS:85054781898
SN - 9783030014179
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 51
EP - 60
BT - Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
PB - Springer-Verlag London Ltd
T2 - 27th International Conference on Artificial Neural Networks, ICANN 2018
Y2 - 4 October 2018 through 7 October 2018
ER -