Improvement of fingerprint retrieval by a statistical classifier

The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simple $k$-nearest neighbor ($k$-NN) classification. However, the $k$-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-$N$ fingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved. © 2010 IEEE.published_or_final_versio

DPD-DFF: a dual phase distributed scheme with double fingerprint fusion for fast and accurate identification in large databases

Nowadays, many companies and institutions need fast and reliable identification systems that are able to deal with very large databases. Fingerprints are among the most used biometric traits for identification. In the current literature there are fingerprint matching algorithms that are focused on efficiency, whilst others are based on accuracy. In this paper we propose a flexible dual phase identification method, called DPD-DFF, that combines two fingers and two matchers within a hybrid fusion scheme to obtain both fast and accurate results. Different alternatives are designed to find a trade-off between runtime and accuracy that can be further tuned with a single parameter. The experiments show that DPD-DFF obtains very competitive results in comparison with the state-of-the-art score fusion techniques, especially when dealing with large databases or impostor fingerprints

Componente de indexación de huellas dactilares basado en características globales

The fingerprint orientation field is a widely used feature for developing indexing strategies. Such feature brings stability and decreases the response times during the identification process. The use of attribute relational graphs and dynamic masks can exploit the features provided by the partitioning scheme. The penetration index and the error rate for each of the proposed strategies were determined. To verify the results obtained were used the databases provided by the Fingerprint Verification Competition. Both search schemes reveal the facilities offered by the orientation field for guiding the searching process; reducing the number of comparisons in 53.24%; proving its stability against different fingerprint image qualities. Based on the results, it was proven that the adoption of such indexing strategies will reduce the response time in the identification module proposed by the Identification and Digital Security Center at the University of Informatics Sciences.El campo de orientación de la huella dactilar como característica global es fundamental para el desarrollo de una estrategia de indexación, aportando estabilidad y disminuyendo los tiempos de respuesta durante el proceso de búsqueda de un sistema de identificación basado en huellas dactilares. El empleo de grafos relacionales de atributos y máscaras dinámicas permite explotar las características brindadas por el esquema de particionado para la búsqueda. De cada una de las estrategias propuestas se determinó el índice de penetración de la base de datos, así como el error en que incurren los algoritmos de indexación implementados. Para corroborar los resultados obtenidos fueron empleados los bancos de datos aportados por la Competencia de verificación de huellas dactilares. La implementación computacional de las estrategias de búsquedas propuestas demuestra las virtudes que ofrece el campo de orientación para dirigir el proceso de búsqueda, logrando reducir el número de comparaciones en un 53.24% como promedio, comportándose de manera estable ante imágenes de huellas dactilares de diferente calidad. Basado en los resultados del estudio se concluyó que la adopción de dichas estrategias de indexación permitirá reducir el tiempo de respuesta en el módulo de identificación de individuos basado en patrones de la huella dactilar propuesto por el Centro de Identificación y Seguridad Digital de la Universidad de las Ciencias Informáticas

A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models

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.Research Projects CAB(CDTI) TIN2011-28488 TIN2013-40765Spanish Government FPU12/0490

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

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.This work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P.

Cancellable biometric using matrix approaches

PhD ThesisCancellable biometrics endeavour to hide the appearance of a biometric image into a transformed template which prevents the outsider from recognising whom the biometric belongs to. Current research into cancellable biometric methodologies concentrates on the details of biometric traits. This approach has a drawback which cannot possibly be implemented with other biometric technology. To address this problem, this thesis contributes to development of a novel concept for the feature transformation of biometric technology, especially for fingerprints, by utilizing several matrix operations to provide an alternative algorithm in order to produce multi-implementation of the cancellable system. The matrix operations generate the feature element of the input fingerprint image in an irrevocable form of output fingerprint template by ignoring the type of biometric traits unique to fingerprints; thus, the cancellable algorithm can be implemented in different biometrics technologies. The implementation offers a new concept in generating a cancellable template by considering a sequential procedure for the fingerprint processing, in order to allow the authentication process to succeed in authenticating an enquired input. For example, a region of interest (RoI) step is required to provide a square form input to support the system working in a matrix domain. Meanwhile, the input fingerprints are mostly in rectangular form. This thesis contributes a new approach to selecting a certain area of a fingerprint by utilizing the density of ridge frequency and orientation. The implementation of these two enhancement steps reduces the excision process of this significant region of the fingerprint by avoiding the involvement of a non-feature area. Meanwhile, to avoid obtaining an un classified fingerprint, this thesis offers a new approach to the fingerprint image classification process entailing three requirements in classifying the fingerprint: the core point and its number, ridge frequency, and ridge direction; whilst the tented arch (TA) is only an additional requirement. The proposed idea increases both the percentage accuracy in classifying fingerprints and time consuming of the system. For Example, the accuracy of the fingerprint classification improves from less than 41 per cent of the fingerprint to 86.48 per cent in average for all of databases.Directorate General of Higher Education of the Ministry of Education and Culture of the Republic of Indonesi

Identification de personnes par fusion de différentes modalités biométriques

This thesis contributes to the resolution of the problems which are related to the analysis of the biometric data outcome from the iris, the fingerprint and the fusion of these two modalities, for person identification. Thus, after the evaluation of those proposed biometric systems, we have shown that the multimodal biometric system based on iris and fingerprint outperforms both monomodal biometric systems based whatsoever on the iris or on the fingerprint.Cette thèse contribue essentiellement à la résolution des problèmes liés à l'analyse des données biométriques issues de l'iris, de l'empreinte digitale et de la fusion de ces deux modalités pour l'identification de personne. Ainsi, après l'évaluation des trois systèmes biométriques proposés, nous avons prouvé que le système biométrique multimodal basé sur l'iris et l'empreinte digitale est plus performant que les deux systèmes biométriques monomodaux basés que se soit sur l'iris ou sur l'empreinte digitale