Authors:
Nicolás Reyes-Reyes
1
;
Marcela González-Araya
2
and
Wladimir Soto-Silva
3
Affiliations:
1
Programa de Doctorado en Sistemas de Ingeniería, Facultad de Ingeniería, Universidad de Talca, Campus Curicó, Camino a Los Niches km 1, Curicó, Chile
;
2
Departamento de Ingeniería Industrial, Facultad de Ingeniería, Universidad de Talca, Campus Curicó, Camino a Los Niches km 1, Curicó, Chile
;
3
Departamento de Computación e Industrias, Facultad de Ciencias de la Ingeniería, Universidad Católica del Maule, Avenida San Miguel 3605, Talca, Chile
Keyword(s):
Fingerprint, Large Classification, Sequential Learning, Extreme Learning Machine, Graphics Processing Unit.
Abstract:
Fingerprint classification allows a biometric identification system to reduce search space in databases and therefore response times. In the literature, fingerprint classification has been addressed through different approaches where deep learning techniques such as convolutional neural networks have been gaining attention. However, the proposed approaches use extremely small data sets for large-scale real-world scenarios that could worsen accuracy rates due to interclass and intraclass variations in fingerprints. For this reason, we proposed a fingerprint classification approach that allows us to address this problem by considering millions of samples. For this purpose, a classifier based on neural networks trained using online sequential extreme learning machines was developed. Likewise, to accelerate the training of the classifier, the matrix operations inside it was run in a graphic processing unit. In order to evaluate our proposal, the approach was tested on three datasets with
more than two million synthetic fingerprint image descriptors. The results are similar in terms of accuracy and computational time to recent approaches but using more than 2.5 million samples.
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