On Multiview Analysis for Fingerprint Liveness Detection

Fingerprint recognition systems, as any other biometric system, can be subject to attacks, which are usually carried out using artificial fingerprints. Several approaches to discriminate between live and fake fingerprint images have been presented to address this issue. These methods usually rely on the analysis of individual features extracted from the fingerprint images. Such features represent different and complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. However, very little work in this direction has been reported in the literature. In this work, we present the results of a preliminary investigation on multiview analysis for fingerprint liveness detection. Experimental results show the effectiveness of such approach, which improves previous results in the literatur

Feature Fusion for Fingerprint Liveness Detection

For decades, fingerprints have been the most widely used biometric trait in identity recognition systems, thanks to their natural uniqueness, even in rare cases such as identical twins. Recently, we witnessed a growth in the use of fingerprint-based recognition systems in a large variety of devices and applications. This, as a consequence, increased the benefits for offenders capable of attacking these systems. One of the main issues with the current fingerprint authentication systems is that, even though they are quite accurate in terms of identity verification, they can be easily spoofed by presenting to the input sensor an artificial replica of the fingertip skin’s ridge-valley patterns. Due to the criticality of this threat, it is crucial to develop countermeasure methods capable of facing and preventing these kind of attacks. The most effective counter–spoofing methods are those trying to distinguish between a "live" and a "fake" fingerprint before it is actually submitted to the recognition system. According to the technology used, these methods are mainly divided into hardware and software-based systems. Hardware-based methods rely on extra sensors to gain more pieces of information regarding the vitality of the fingerprint owner. On the contrary, software-based methods merely rely on analyzing the fingerprint images acquired by the scanner. Software-based methods can then be further divided into dynamic, aimed at analyzing sequences of images to capture those vital signs typical of a real fingerprint, and static, which process a single fingerprint impression. Among these different approaches, static software-based methods come with three main benefits. First, they are cheaper, since they do not require the deployment of any additional sensor to perform liveness detection. Second, they are faster since the information they require is extracted from the same input image acquired for the identification task. Third, they are potentially capable of tackling novel forms of attack through an update of the software. The interest in this type of counter–spoofing methods is at the basis of this dissertation, which addresses the fingerprint liveness detection under a peculiar perspective, which stems from the following consideration. Generally speaking, this problem has been tackled in the literature with many different approaches. Most of them are based on first identifying the most suitable image features for the problem in analysis and, then, into developing some classification system based on them. In particular, most of the published methods rely on a single type of feature to perform this task. Each of this individual features can be more or less discriminative and often highlights some peculiar characteristics of the data in analysis, often complementary with that of other feature. Thus, one possible idea to improve the classification accuracy is to find effective ways to combine them, in order to mutually exploit their individual strengths and soften, at the same time, their weakness. However, such a "multi-view" approach has been relatively overlooked in the literature. Based on the latter observation, the first part of this work attempts to investigate proper feature fusion methods capable of improving the generalization and robustness of fingerprint liveness detection systems and enhance their classification strength. Then, in the second part, it approaches the feature fusion method in a different way, that is by first dividing the fingerprint image into smaller parts, then extracting an evidence about the liveness of each of these patches and, finally, combining all these pieces of information in order to take the final classification decision. The different approaches have been thoroughly analyzed and assessed by comparing their results (on a large number of datasets and using the same experimental protocol) with that of other works in the literature. The experimental results discussed in this dissertation show that the proposed approaches are capable of obtaining state–of–the–art results, thus demonstrating their effectiveness

Contributions to non-conventional biometric systems : improvements on the fingerprint, facial and handwriting recognition approach

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2021.Os sistemas biométricos são amplamente utilizados pela sociedade. A maioria das aplicações desses sistemas está associada à identificação civil e à investigação criminal. No entanto, com o tempo, o desempenho dos métodos tradicionais de biometria está chegando ao limite. Neste contexto, sistemas biométricos emergentes ou não convencionais estão ganhando importância. Embora promissores, novos sistemas, assim como qualquer nova tecnologia, trazem consigo não apenas potencialidades, mas também fragilidades. Este trabalho apresenta contribuições para três importantes sistemas biométricos não convencionais (SBNC): impressão digital, reconhecimento facial e reconhecimento de escrita. No que diz respeito às impressões digitais, este trabalho apresenta um novo método para detectar a vida em dispositivos de impressão digital multivista sem toque, utilizando descritores de textura e redes neurais artificiais. Com relação ao reconhecimento facial, um método de reconhecimento de faces baseado em algoritmos de característica invariante à escala (SIFT e SURF) que opera sem a necessidade de treinamento prévio do classificador e que realiza o rastreamento de indivíduos em ambientes não controlados é apresentado. Finalmente, um método de baixo custo que usa sinais de acelerômetro e giroscópio obtidos a partir de um sensor acoplado a canetas convencionais para realizar o reconhecimento em tempo real de assinaturas é apresentado. Resultados mostram que os métodos propostos são promissores e que juntos podem contribuir para o aprimoramento dos SBNCCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Biometric systems are widely used by society. Most applications are associated with civil identification and criminal investigation. However, over time, traditional methods of performing biometrics have been reaching their limits. In this context, emerging or nonconventional biometric systems (NCBS) are gaining ground. Although promising, new systems, as well as any new technology, bring not only potentialities but also weaknesses. This work presents contributions to three important non-conventional biometric systems: fingerprint, facial, and handwriting recognition. With regard to fingerprints, this work presents a novel method for detecting life on Touchless Multi-view Fingerprint Devices, using Texture Descriptors and Artificial Neural Networks. With regard to face recognition, a facial recognition method is presented, based on Scale Invariant Feature Algorithms (SIFT and SURF), that operates without the need of previous training of a classifier and can be used to track individuals in an unconstrained environment. Finally, a low-cost on-line handwriting signature recognition method that uses accelerometer and gyroscope signals obtained from a sensor coupled to conventional pens to identify individuals in real time is presented. Results show that the proposed methods are promising and that together may contribute to the improvement of the NCB

An overview of touchless 2D fingerprint recognition

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

Dataset Pre-Processing and Artificial Augmentation, Network Architecture and Training Parameters used in Appropriate Training of Convolutional Neural Networks for Classification Based Computer Vision Applications: A Survey

Training a Convolutional Neural Network (CNN) based classifier is dependent on a large number of factors. These factors involve tasks such as aggregation of apt dataset, arriving at a suitable CNN network, processing of the dataset, and selecting the training parameters to arrive at the desired classification results. This review includes pre-processing techniques and dataset augmentation techniques used in various CNN based classification researches. In many classification problems, it is usually observed that the quality of dataset is responsible for proper training of CNN network, and this quality is judged on the basis of variations in data for every class. It is not usual to find such a pre-made dataset due to many natural concerns. Also it is recommended to have a large dataset, which is again not usually made available directly as a dataset. In some cases, the noise present in the dataset may not prove useful for training, while in others, researchers prefer to add noise to certain images to make the network less vulnerable to unwanted variations. Hence, researchers use artificial digital imaging techniques to derive variations in the dataset and clear or add noise. Thus, the presented paper accumulates state-of-the-art works that used the pre-processing and artificial augmentation of dataset before training. The next part to data augmentation is training, which includes proper selection of several parameters and a suitable CNN architecture. This paper also includes such network characteristics, dataset characteristics and training methodologies used in biomedical imaging, vision modules of autonomous driverless cars, and a few general vision based applications

Detecção de fraudes em leitores de impressões digitais sem contato utilizando descritores de texturas e redes neurais artificiais

Monografia (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.Com o advento dos sistemas biométricos de impressão digital, surgem também as mais diversas técnicas de ataque que visam enganar a segurança imposta. Quando um atacante investe contra um sistema, três possíveis situações merecem destaque. Em uma delas, o atacante fornece uma impressão digital falsa com o objetivo de se passar por um terceiro. Na outra, o atacante altera sua impressão digital de forma a não ser reconhecido. Ainda, há situações nas quais um usuário insere objetos no leitor, ou seja, não são apresentadas impressões digitais. Para que tais tentativas de fraude sejam combatidas, este trabalho apresenta um método capaz de classificar imagens provenientes de um leitor biométrico de impressões digitais sem contato em categorias que indicam a presença de dedos reais, dedos oclusos e objetos que não são dedos. A classificação é realizada com uma combinação de redes neurais artificiais e os descritores de textura ILBP e GLCM. _____________________________________________________________________________ ABSTRACTAlong with the advent of biometric systems, many techniques have been created in order to trick them. Many times, these techniques are performed at the sensor level and, according to the way it is done, they may be classified into 3 subcategories. Two of them are worth mentioning for the purpose of this paper. In one of them, the attacker provides a fake fingerprint to make the system believe that the attacker is a valid user. In the other, the attacker obfuscates his fingerprint so that the system will not be able to identify him. There is also the situation that a user presents an object to the sensor, in which case there is no fingerprint. This paper proposes an anti-spoofing method that classifies images acquired from a touchless fingreprint biometric sensor into three categories: real fingerprint, obfuscated fingerprint or not even a finger. The proposed method makes use of artificial neural networks and a combination of two texture descriptors: ILBP and GLCM

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Enhanced Augmented Reality Framework for Sports Entertainment Applications

Augmented Reality (AR) superimposes virtual information on real-world data, such as displaying useful information on videos/images of a scene. This dissertation presents an Enhanced AR (EAR) framework for displaying useful information on images of a sports game. The challenge in such applications is robust object detection and recognition. This is even more challenging when there is strong sunlight. We address the phenomenon where a captured image is degraded by strong sunlight. The developed framework consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player detection, face detection, recognition of players, and display of personal information of players. First, an algorithm based on Multi-Scale Retinex (MSR) is proposed for image enhancement. For the tasks of player and face detection, we use adaptive boosting algorithm with Haar-like features for both feature selection and classification. The player face recognition algorithm uses adaptive boosting with the LDA for feature selection and nearest neighbor classifier for classification. The framework can be deployed in any sports where a viewer captures images. Display of players-specific information enhances the end-user experience. Detailed experiments are performed on 2096 diverse images captured using a digital camera and smartphone. The images contain players in different poses, expressions, and illuminations. Player face recognition module requires players faces to be frontal or up to ?350 of pose variation. The work demonstrates the great potential of computer vision based approaches for future development of AR applications.COMSATS Institute of Information Technolog