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A Framework in C# for Fingerprint Verification

2 Jan 2019CPOL11 min read 1.6M   143.5K   672  
In this article, we introduce a framework in C# for fingerprint verification, we briefly explain how to perform fingerprint verification experiments and how to integrate your algorithms to the framework.
/*
 * Created by: Andrés Eduardo Gutiérrez Rodríguez (andres@bioplantas.cu)
 *             Miguel Angel Medina Pérez (miguel.medina.perez@gmail.com)
 * Created: 
 * Comments by: Miguel Angel Medina Pérez (miguel.medina.perez@gmail.com)
 */

using System;
using System.Collections.Generic;
using System.Drawing;
using PatternRecognition.FingerprintRecognition.Core;
using PatternRecognition.FingerprintRecognition.FeatureRepresentation;
using PatternRecognition.FingerprintRecognition.Matchers;

namespace PatternRecognition.FingerprintRecognition.FeatureExtractors
{

    /// <summary>
    ///     A class to extract the features used by <see cref="QYW"/> to match fingerprints.
    /// </summary>
    /// <remarks>
    ///     <para>
    ///         In order to extract features from an image, the properties <see cref="MtiaExtractor"/> and <see cref="OrImgExtractor"/> must be assigned.
    ///     </para>
    ///     <para>
    ///         The features can also be extracted specifying a minutia list and an orientation image.
    ///     </para>
    /// </remarks>
    public class Qi2005FeatureExtractor : FeatureExtractor<Qi2005Features>
    {
        /// <summary>
        ///     The minutia list extractor used to compute <see cref="Qi2005Features"/> in the method <see cref="ExtractFeatures(Bitmap)"/>.
        /// </summary>
        public IFeatureExtractor<List<Minutia>> MtiaExtractor { set; get; }

        /// <summary>
        ///     The orientation image extractor used to compute <see cref="Qi2005Features"/> in the method <see cref="ExtractFeatures(Bitmap)"/>.
        /// </summary>
        public IFeatureExtractor<OrientationImage> OrImgExtractor { set; get; }

        /// <summary>
        ///     Extract features of type <see cref="Qi2005Features"/> from the specified image.
        /// </summary>
        /// <remarks>
        ///     This method uses the properties <see cref="MtiaExtractor"/> and <see cref="OrImgExtractor"/> to extract features, so it raises an exception if any of these properties is not assigned.
        /// </remarks>
        /// <exception cref="InvalidOperationException">
        ///     Thrown when the minutia list extractor is not assigned or the orientation image extractor is not assigned.
        /// </exception>
        /// <param name="image">The source image to extract features from.</param>
        /// <returns>
        ///     Features of type <see cref="Qi2005Features"/> extracted from the specified image.
        /// </returns>
        public override Qi2005Features ExtractFeatures(Bitmap image)
        {
            try
            {
                var mtiae = MtiaExtractor.ExtractFeatures(image);
                var dImg = OrImgExtractor.ExtractFeatures(image);

                return new Qi2005Features(mtiae, dImg);
            }
            catch(Exception e)
            {
                if (MtiaExtractor == null)
                    throw new InvalidOperationException("Can not extract Qi2005Features: Unassigned minutia list extractor!", e);
                if (OrImgExtractor == null)
                    throw new InvalidOperationException("Can not extract Qi2005Features: Unassigned orientation image extractor!", e);
                throw;
            }
        }

        /// <summary>
        ///     Extract features of type <see cref="Qi2005Features"/> from the specified minutia list and orientation image.
        /// </summary>
        /// <param name="mtiae">
        ///     The minutia list to extract the features from.
        /// </param>
        /// <param name="orImg">
        ///     The skeleton image to extract the features from.
        /// </param>
        /// <returns>
        ///     Features of type <see cref="Qi2005Features"/> extracted from the specified minutiae and skeleton image.
        /// </returns>
        public Qi2005Features ExtractFeatures(List<Minutia> mtiae, OrientationImage orImg)
        {
            return new Qi2005Features(mtiae, orImg);
        }
    }
}

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License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


Written By
Tecnológico de Monterrey
Mexico Mexico
I received my B.S. and M.S. degrees from the University of Ciego de Ávila, Cuba, in 2007 and I received my PhD. degree in 2014 from the National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico.

I have developed software to solve pattern recognition problems. A successful example is the algorithm DMC which is the most accurate (according to EER) among those which compare both fingerprints and palmprints in the international competition FVC-onGoing.

I have been involved in several research projects about pattern recognition and I have published tens of papers in referenced journals such as "Pattern Recognition," "Knowledge-Based Systems," "Information Sciences", and "IEEE Transactions on Information Forensics and Security."

Written By
Cuba Cuba
Milton García-Borroto is graduated from Las Villas Central University, Cuba, in 2000. He received the M.S. degree in 2007 from the National Institute of Astrophisics, Optics and Electronics, Mexico, where he continues his studies toward a Ph.D. degree. His research interests are pattern recognition and biometry.

Relevant papers:
1. M. García-Borroto, J. F. Martinez Trinidad, J. A. Carrasco Ochoa, M. A. Medina-Pérez, and J. Ruiz-Shulcloper. LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification. Pattern Recognition vol. 43, pp. 3025-3034, 2010.
2. M. García-Borroto, J. F. Martinez Trinidad, J. A. Carrasco Ochoa. A New Emerging Pattern Mining Algorithm and Its Application in Supervised Classification. M.J. Zaki et al. (Eds.): PAKDD 2010, Part I, Lecture Notes in Artificial Intelligence, vol. 6118, pp. 150–157, 2010.
3. M. A. Medina-Pérez, A. Gutiérrez-Rodríguez, and M. García-Borroto, "Improving Fingerprint Matching Using an Orientation-Based Minutia Descriptor," Lecture Notes in Computer Science, vol. 5856, pp. 121-128, 2009.
4. M. García-Borroto, Y. Villuendas-Rey, J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad, "Finding Small Consistent Subset for the Nearest Neighbor Classifier Based on Support Graphs," Lecture Notes in Computer Science, vol. 5856, pp. 465-472, 2009.
5. M. García-Borroto, Y. Villuendas-Rey, J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad, "Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers," Lecture Notes in Computer Science, vol. 5856, pp. 489-496, 2009.
6. M. A. Medina-Pérez, M. García-Borroto, and J. Ruiz-Shulcloper, "Object Selection Based on Subclass Error Correcting for ALVOT," Lecture Notes in Computer Science, vol. 4756, pp. 496-505, 2007.

Andres Eduardo Gutierrez Rodriguez is graduated from Las Villas Central University, Cuba, in 2006. He received the M.S. degree in 2009 from the University of Ciego de Ávila, Cuba. His research interests are pattern recognition and biometry.

Relevant papers:

-M. A. Medina-Pérez, A. Gutiérrez-Rodríguez, and M. García-Borroto, "Improving Fingerprint Matching Using an Orientation-Based Minutia Descriptor," Lecture Notes in Computer Science, vol. 5856, pp. 121-128, 2009.
-A. E. Gutierrez-Rodriguez, M. A. Medina-Perez, J. F. Martinez-Trinidad, J. A. Carrasco-Ochoa, and M. Garcia-Borroto, "New Dissimilarity Measures for Ultraviolet Spectra Identification," Lecture Notes in Computer Science, vol. 6256, pp. 220-229, 2010.

Written By
Program Manager
Spain Spain
Octavio Loyola-González received his PhD degree in Computer Science from the National Institute for Astrophysics, Optics, and Electronics, Mexico. He has won several awards from different institutions due to his research work on applied projects; consequently, he is a Member of the National System of Researchers in Mexico (Rank1). He worked as a distinguished professor and researcher at Tecnologico de Monterrey, Campus Puebla, for undergraduate and graduate programs of Computer Sciences. Currently, he is responsible for running Machine Learning & Artificial Intelligence practice inside Stratesys., where he is involved in the development and implementation using analytics and data mining. He has outstanding experience in the fields of big data & pattern recognition, cloud computing, IoT, and analytical tools to apply them in sectors where he has worked for as Banking & Insurance, Retail, Oil&Gas, Agriculture, Cybersecurity, Biotechnology, and Dactyloscopy. From these applied projects, Dr. Loyola-González has published several books and papers in well-known journals, and he has several ongoing patents as manager and researcher in Stratesys.

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