6 research outputs found

    An overview of touchless 2D fingerprint recognition

    Get PDF
    Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work

    Design and implementation of an artificial neural network applied to finger bad-positioning detection on touchless multiview fingerprints devices

    Get PDF
    This paper presents a method based on Artificial Neural Network that evaluates the rotational bad-positioning of fingers on touchless multiview fingerprinting devices. The objective is to determine whether the finger is rotated or not, since a proper positioning of the finger is mandatory for high fingerprint matching rates. A test set of 9000 acquired images has being used to train, validate and test the proposed multilayer Artificial Neural Network classifier. To our knowledge, there is no definitive method that addressed the problem of fingerprint quality on touchless multiview scanners. The proposed finger rotation detection here presented is one of the steps that must be taken into account if a future automatic image quality assessment method is to be considered. Average results show that: (a) our classifier correctly identifies bad-positioning in approximately 94% of cases; and (b) if bad-positioning is detected, the rotation angle is correctly estimated in 90% evaluations

    Toward unconstrained fingerprint recognition : a fully touchless 3-D system based on two views on the move

    Get PDF
    Touchless fingerprint recognition systems do not require contact of the finger with any acquisition surface and thus provide an increased level of hygiene, usability, and user acceptability of fingerprint-based biometric technologies. The most accurate touchless approaches compute 3-D models of the fingertip. However, a relevant drawback of these systems is that they usually require constrained and highly cooperative acquisition methods. We present a novel, fully touchless fingerprint recognition system based on the computation of 3-D models. It adopts an innovative and less-constrained acquisition setup compared with other previously reported 3-D systems, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving. To compensate for possible differences in finger placement, we propose novel algorithms for computing 3-D models of the shape of a finger. Moreover, we present a new matching strategy based on the computation of multiple touch-compatible images. We evaluated different aspects of the biometric system: acceptability, usability, recognition performance, robustness to environmental conditions and finger misplacements, and compatibility and interoperability with touch-based technologies. The proposed system proved to be more acceptable and usable than touch-based techniques. Moreover, the system displayed satisfactory accuracy, achieving an equal error rate of 0.06% on a dataset of 2368 samples acquired in a single session and 0.22% on a dataset of 2368 samples acquired over the course of one year. The system was also robust to environmental conditions and to a wide range of finger rotations. The compatibility and interoperability with touch-based technologies was greater or comparable to those reported in public tests using commercial touchless devices

    An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

    Get PDF
    The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

    Aquisição de base de dados e casamento multivista de impressões digitais fotográficas sem contato

    Get PDF
    Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2018.Para cada leitura de digital, enquanto o sensor de toque captura uma única imagem, o sensor sem toque captura três imagens a partir das quais uma quarta imagem é criada automaticamente, unindo as três vistas em uma foto única que funciona como uma panorâmica da digital. Partindo da ideia de que parte da informação é perdida ao combinarmos as três vistas em uma imagem única, esse trabalho propõe uma solução em que, ao invés de aplicarmos o algoritmo de casamento de impressões digitais diretamente à imagem panorâmica, aplica-o à cada uma das três imagens originais e, por meio de uma fórmula matemática, definida pela aplicação de um algoritmo genético, combina os três resultados em um resultado único. Para testar a eficiência dessa abordagem, comparamos os resultados obtidos com a aplicação desse mesmo algoritmo de casamento de digitais em um banco de dados composto pelas digitais panorâmicas sem contato, um banco composto somente pelas vistas centrais obtidas pelo sensor sem contato e em outro banco composto unicamente por impressões digitais recolhidas por um sensor com contato. Apesar de não ter conseguido superar o desempenho das digitais com contato ou das digitais panorâmicas geradas automaticamente, o método proposto apresentou uma taxa de erro relativamente baixa, com um ERR de 4.78% para a base de dados analisada.For each fingerprint reading, while the touch-based sensor captures a single image, the contactless sensor gets three images, one for each of its cameras. A fourth image is automatically generated, joining the three views into a single panoramic view. Based on the idea that some of the information is lost when the system combines the three views into a single image, this paper proposes a solution where, instead of applying the fingerprint matching algorithm to the panoramic image, we aplly it to each one of the three views and merge the response into a single result. This unified result is given by a mathematical combination, using parameters defined by a genetic algorithm. To test the efficiency of this approach, we compared the results obtained with the application of the same matching algorithm in a database composed of panoramic views, another database composed only of the central views obtained by the contactless sensor and one composed of fingerprints collected by a touch-based sensor. Although it was not able to overcome the contact-based or panoramic fingerprint performance, the proposed method presented a relatively low error rate, with an ERR of 4.78% for the analyzed database

    Unimodal and multimodal biometric sensing systems : a review

    Get PDF
    Biometric systems are used for the verification and identification of individuals using their physiological or behavioral features. These features can be categorized into unimodal and multimodal systems, in which the former have several deficiencies that reduce the accuracy of the system, such as noisy data, inter-class similarity, intra-class variation, spoofing, and non-universality. However, multimodal biometric sensing and processing systems, which make use of the detection and processing of two or more behavioral or physiological traits, have proved to improve the success rate of identification and verification significantly. This paper provides a detailed survey of the various unimodal and multimodal biometric sensing types providing their strengths and weaknesses. It discusses the stages involved in the biometric system recognition process and further discusses multimodal systems in terms of their architecture, mode of operation, and algorithms used to develop the systems. It also touches on levels and methods of fusion involved in biometric systems and gives researchers in this area a better understanding of multimodal biometric sensing and processing systems and research trends in this area. It furthermore gives room for research on how to find solutions to issues on various unimodal biometric systems.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2017Electrical, Electronic and Computer Engineerin
    corecore