A Survey of Fingerprint Classification Part II: Experimental Analysis and Ensemble Proposal
Metadatos
Afficher la notice complèteAuteur
Galar, Mikel; Peralta, Daniel; Triguero, Isaac; García López, Salvador; Benítez Sánchez, José Manuel; Herrera Triguero, FranciscoEditorial
ELSEVIER
Materia
Fingerprint classification Feature extraction Classification Fingerprint recognition SVM Neural networks Ensemble Orientation Map Singular Points Experimental evaluation
Date
2015Referencia bibliográfica
Galar, M., Derrac, J., Peralta, D., Triguero, I., Paternain, D., Lopez-Molina, C., . . . Herrera, F. b. (2015). A survey of fingerprint classification part II: Experimental analysis and ensemble proposal. Knowledge-Based Systems, 81, 98-116. [doi: 10.1016/j.knosys.2015.02.015]
Patrocinador
Research Projects CAB(CDTI) TIN2011-28488 TIN2013-40765; Spanish Government FPU12/04902Résumé
In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among
the reviewed methods would perform better in a real implementation we end up in a discussion which showed
the difficulty in answering this question. No previous comparison exists in the literature and comparisons
among papers are done with different experimental frameworks. Moreover, the difficulty in implementing
published methods was stated due to the lack of details in their description, parameters and the fact that no
source code is shared. For this reason, in this paper we will go through a deep experimental study following
the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant
feature extraction methods according to the explanations found in the corresponding papers and we have
tested their performance with different classifiers, including those specific proposals made by the authors.
Our aim is to develop an objective experimental study in a common framework, which has not been done
before and which can serve as a baseline for future works on the topic. This way, we will not only test
their quality, but their reusability by other researchers and will be able to indicate which proposals could be
considered for future developments. Furthermore, we will show that combining different feature extraction
models in an ensemble can lead to a superior performance, significantly increasing the results obtained by
individual models.