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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/44550

Title: Minería de datos en computación de altas prestaciones para identificación en base a huellas dactilares
Authors: Peralta, Daniel
Direction: Herrera, Francisco
Benítez Sánchez, José Manuel
Collaborator: Universidad de Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial
Issue Date: 2016
Submitted Date: 26-Sep-2016
Abstract: This thesis starts by presenting a deep study of the scientific literature on minutiae-based local matching matching techniques, establishing a taxonomy of the available local structures and consolidation methods, and highlighting the main advantages and drawbacks of each of them. Then, we will present a minutiae filtering algorithm that removes spurious or misleading minutiae to improve both the identification time and the accuracy of the recognition process. After that, we will describe two frameworks for massively parallel fingerprint identification, which are able to execute diffierent matching algorithms adapting to the underlying hardware for maximum performance and full scalability. We will also develop a framework to combine the information of two fingerprints and the capabilities of two diferent matching algorithms to address both problems that hinder identification in large databases: the high identification time and the loss of accuracy. Finally, we describe a new classification strategy to reduce the penetration rate of the identification. Finally After this introduction section, Section 2 describes in detail the background of the main areas addressed in this thesis: fingerprint feature extraction (Section 2.1), fingerprint identification (Section 2.2), high performance computing (Section 2.3), database penetration reduction and fingerprint classification (Section 2.4) and information fusion for fingerprint identification (Section 2.5). After that, Section 3 presents the justification of this memory, describing the open problems addressed throughout this thesis. The objectives pursued to address these problems are detailed in Section 4, along with the methodology followed along the thesis in Section 5. Section 6 summarizes the works that compose this memory, while Section 7 presents the results obtained in them, performing an analysis in relation with the tackled objectives and how they have been reached. Section 8 presents the conclusions after the work carried out for this thesis. Finally, in Section 9 we point out several future lines of work that have been derived from the results achieved.
Sponsorship: Tesis Univ. Granada. Programa Oficial de Doctorado en: Tecnologías de la Información y la Comunicación
Becas de Formacióon de Profesorado Universitario del Ministerio de Educación y Ciencia, en su Resolución del 28 de Febrero de 2013, bajo la referencia FPU12/04902.
Publisher: Universidad de Granada
Keywords: Minería de datos
Identificación
Dactiloscopia
Computación de altas prestaciones
Proceso electrónico de datose
UDC: 681.3
3325
URI: http://hdl.handle.net/10481/44550
ISBN: 9788491630272
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Peralta Cámara, D. Minería de datos en computación de altas prestaciones para identificación en base a huellas dactilares. Granada: Universidad de Granada, 2016. [http://hdl.handle.net/10481/44550]
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