Detailed Identification of Fingerprints Using Convolutional Neural Networks

Yahaya Isah Shehu; Ariel Ruiz-Garcia; Vasile Palade; Anne James

doi:10.1109/ICMLA.2018.00187

Detailed Identification of Fingerprints Using Convolutional Neural Networks

Yahaya Isah Shehu, Ariel Ruiz-Garcia, Vasile Palade, Anne James

Faculty of Engineering, Environment & Computing

Nottingham Trent University

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

11 Citations (Scopus)

Abstract

Fingerprints, as one of the most widely used biometric modalities, can be used to identify and distinguish between genders. Gender classification is very important in reducing the time when investigating criminal offenders and gender impersonation. In this work, we use deep Convolutional Neural Networks (CNNs) to not only classify fingerprints by gender, but also identify individual hands and fingers. Transfer learning is employed to speed up the training of the CNN. The CNN achieves an accuracy of 75.2%, 93.5%, and 76.72% for the classification of gender, hand, and fingers, respectively. These results obtained using our publicly available Sokoto Coventry Fingerprint Dataset (SOCOFing) serve as benchmark classification results on this dataset.

Original language	English
Title of host publication	Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1161-1165
Number of pages	5
ISBN (Electronic)	978-1-5386-6805-4
ISBN (Print)	978-1-5386-6806-1
DOIs	https://doi.org/10.1109/ICMLA.2018.00187
Publication status	Published - 17 Jan 2019
Event	17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States Duration: 17 Dec 2018 → 20 Dec 2018

Conference

Conference	17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
Country/Territory	United States
City	Orlando
Period	17/12/18 → 20/12/18

Keywords

Convolutional Neural Networks
Fingerprints
Gender Classification
Transfer Learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Safety, Risk, Reliability and Quality
Signal Processing
Decision Sciences (miscellaneous)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICMLA.2018.00187

Cite this

Detailed Identification of Fingerprints Using Convolutional Neural Networks. / Shehu, Yahaya Isah; Ruiz-Garcia, Ariel; Palade, Vasile et al.

Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1161-1165 8614212.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Shehu, YI, Ruiz-Garcia, A, Palade, V & James, A 2019, Detailed Identification of Fingerprints Using Convolutional Neural Networks. in Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018., 8614212, Institute of Electrical and Electronics Engineers Inc., pp. 1161-1165, 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Orlando, United States, 17/12/18. https://doi.org/10.1109/ICMLA.2018.00187

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abstract = "Fingerprints, as one of the most widely used biometric modalities, can be used to identify and distinguish between genders. Gender classification is very important in reducing the time when investigating criminal offenders and gender impersonation. In this work, we use deep Convolutional Neural Networks (CNNs) to not only classify fingerprints by gender, but also identify individual hands and fingers. Transfer learning is employed to speed up the training of the CNN. The CNN achieves an accuracy of 75.2%, 93.5%, and 76.72% for the classification of gender, hand, and fingers, respectively. These results obtained using our publicly available Sokoto Coventry Fingerprint Dataset (SOCOFing) serve as benchmark classification results on this dataset.",

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AB - Fingerprints, as one of the most widely used biometric modalities, can be used to identify and distinguish between genders. Gender classification is very important in reducing the time when investigating criminal offenders and gender impersonation. In this work, we use deep Convolutional Neural Networks (CNNs) to not only classify fingerprints by gender, but also identify individual hands and fingers. Transfer learning is employed to speed up the training of the CNN. The CNN achieves an accuracy of 75.2%, 93.5%, and 76.72% for the classification of gender, hand, and fingers, respectively. These results obtained using our publicly available Sokoto Coventry Fingerprint Dataset (SOCOFing) serve as benchmark classification results on this dataset.

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Detailed Identification of Fingerprints Using Convolutional Neural Networks

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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