On the use of convolutional neural networks for robust classification of multiple fingerprint captures
- Author
- Daniel Peralta Cámara (UGent) , Isaac Triguero, Salvador García, Yvan Saeys (UGent) , Jose M Benitez and Francisco Herrera
- Organization
- Abstract
- Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low-quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification.
- Keywords
- SUPPORT VECTOR MACHINES, FEATURE-EXTRACTION, PATTERN-CLASSIFICATION, SINGULAR POINTS, LEVEL FUSION, RECOGNITION, IDENTIFICATION, VERIFICATION, CLASSIFIERS, DATABASE, Convolutional neural networks, fingerprint classification, deep, learning, deep neural networks
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8539885
- MLA
- Peralta, Daniel et al. “On the Use of Convolutional Neural Networks for Robust Classification of Multiple Fingerprint Captures.” INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 33.1 (2018): 213–230. Print.
- APA
- Peralta, D., Triguero, I., García, S., Saeys, Y., Benitez, J. M., & Herrera, F. (2018). On the use of convolutional neural networks for robust classification of multiple fingerprint captures. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 33(1), 213–230.
- Chicago author-date
- Peralta, Daniel, Isaac Triguero, Salvador García, Yvan Saeys, Jose M Benitez, and Francisco Herrera. 2018. “On the Use of Convolutional Neural Networks for Robust Classification of Multiple Fingerprint Captures.” International Journal of Intelligent Systems 33 (1): 213–230.
- Chicago author-date (all authors)
- Peralta, Daniel, Isaac Triguero, Salvador García, Yvan Saeys, Jose M Benitez, and Francisco Herrera. 2018. “On the Use of Convolutional Neural Networks for Robust Classification of Multiple Fingerprint Captures.” International Journal of Intelligent Systems 33 (1): 213–230.
- Vancouver
- 1.Peralta D, Triguero I, García S, Saeys Y, Benitez JM, Herrera F. On the use of convolutional neural networks for robust classification of multiple fingerprint captures. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS. 2018;33(1):213–30.
- IEEE
- [1]D. Peralta, I. Triguero, S. García, Y. Saeys, J. M. Benitez, and F. Herrera, “On the use of convolutional neural networks for robust classification of multiple fingerprint captures,” INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, vol. 33, no. 1, pp. 213–230, 2018.
@article{8539885,
abstract = {{Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low-quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification.}},
author = {{Peralta Cámara, Daniel and Triguero, Isaac and García, Salvador and Saeys, Yvan and Benitez, Jose M and Herrera, Francisco}},
issn = {{0884-8173}},
journal = {{INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS}},
keywords = {{SUPPORT VECTOR MACHINES,FEATURE-EXTRACTION,PATTERN-CLASSIFICATION,SINGULAR POINTS,LEVEL FUSION,RECOGNITION,IDENTIFICATION,VERIFICATION,CLASSIFIERS,DATABASE,Convolutional neural networks,fingerprint classification,deep,learning,deep neural networks}},
language = {{eng}},
number = {{1}},
pages = {{213--230}},
title = {{On the use of convolutional neural networks for robust classification of multiple fingerprint captures}},
url = {{http://dx.doi.org/10.1002/int.21948}},
volume = {{33}},
year = {{2018}},
}
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