A Survey of Fingerprint Classification

This Website contains complementary material to the papers:

M. Galar, J. Derrac, D. Peralta, I. Triguero, D. Paternain, C. Lopez-Molina, S. García, J.M. Benítez, M. Pagola, E. Barrenechea, H. Bustince, and F. Herrera, A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models . Knowledge-based Systems, submitted.

M. Galar, J. Derrac, D. Peralta, I. Triguero, D. Paternain, C. Lopez-Molina, S. García, J.M. Benítez, M. Pagola, E. Barrenechea, H. Bustince, and F. Herrera, A Survey of Fingerprint Classification Part II: Experimental Analysis and Ensemble Proposal. Knowledge-based Systems, submitted.

Summary:

Abstract
Source code for feature extraction and specific classification approaches
1. Feature extractor
2. Specific classification models
Datasets
1. SFinGe
2. NIST-4

Abstract

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 points detection and 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 points 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 as well as a new method based on their combination is presented.

M. Galar, J. Derrac, D. Peralta, I. Triguero, D. Paternain, C. Lopez-Molina, S. García, J.M. Benítez, M. Pagola, E. Barrenechea, H. Bustince, and F. Herrera, A Survey of Fingerprint Classification Part II: Experimental Analysis and Ensemble Proposal. Knowledge-based Systems, submitted.

Abstract

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. 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.

Source code for feature extraction and specific classification approaches

Feature extraction methods

The source code of the feature extraction methods implemented and considered in the empirical study can be obtained in the following URL FeatureExtractor.

Sepecific classification models

The source code of the specific classification models implemented can be found in Specific classifiers. It also includes the imputation methods needed for Leung's feature extraction method.

Datasets

In this section, we provide the datasets obtained using the different feature extractors with the five databases tested (3 of SFinGe and 2 of NIST-4) so that they can be tested by other researchers with different classifiers, in such a way that the experimental study becomes reproducible.