A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models Galar, Mikel Peralta, Daniel Triguero, Isaac García López, Salvador Benítez Sánchez, José Manuel Herrera Triguero, Francisco Fingerprint classification Feature extraction Classification Fingerprint recognition SVM Neural networks Ensemble Orientation Map Singular Points This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented. 2020-12-16T09:10:29Z 2020-12-16T09:10:29Z 2015 info:eu-repo/semantics/article Galar, M., Derrac, J., Peralta, D., Triguero, I., Paternain, D., Lopez-Molina, C., . . . Herrera, F. (2015). A survey of fingerprint classification part I: Taxonomies on feature extraction methods and learning models. Knowledge-Based Systems, 81, 76-97. [doi:10.1016/j.knosys.2015.02.008] http://hdl.handle.net/10481/64942 10.1016/j.knosys.2015.02.008 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España ELSEVIER