A Survey of Fingerprint Classification Part II: Experimental Analysis and Ensemble Proposal
MetadataShow full item record
AuthorGalar, Mikel; Peralta, Daniel; Triguero, Isaac; García López, Salvador; Benítez Sánchez, José Manuel; Herrera Triguero, Francisco
Fingerprint classificationFeature extractionClassificationFingerprint recognitionSVMNeural networksEnsembleOrientation MapSingular PointsExperimental evaluation
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]
SponsorshipResearch Projects CAB(CDTI) TIN2011-28488 TIN2013-40765; Spanish Government FPU12/04902
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.