Um novo arcabouço para análise de qualidade de imagens de impressões digitais de alta resolução

Orientador: Neucimar Jerônimo LeiteTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A falta de robustez referente à degradação de qualidade de conjuntos de características extraídas de padrões de cristas-e-vales, contidos na epiderme dos dedos humanos, é uma das questões em aberto na análise de imagens de impressões digitais, com implicações importantes em problemas de segurança, privacidade e fraude de identificação. Neste trabalho, introduzimos uma nova metodologia para analisar a qualidade de conjuntos de características de terceiro nível em imagens de impressões digitais representados, aqui, por poros de transpiração. A abordagem sugerida leva em conta a interdependência espacial entre as características consideradas e algumas transformações básicas envolvendo a manipulação de processos pontuais e sua análise a partir de ferramentas anisotrópicas. Foram propostos dois novos algoritmos para o cálculo de índices de qualidade que se mostraram eficazes na previsão da qualidade da correspondência entre as impressões e na definição de pesos de filtragem de características de baixa qualidade a ser empregado num processo de identificação. Para avaliar experimentalmente o desempenho destes algoritmos e suprir a ausência de uma base de dados com níveis de qualidade controlados, criamos uma base de dados com diferentes recursos de configuração e níveis de qualidade. Neste trabalho, propusemos ainda um método para reconstruir imagens de fase da impressão digital a partir de um dado conjunto de coordenadas de poros. Para validar esta idéia sob uma perspectiva de identificação, consideramos conjuntos de minúcias presentes nas imagens reconstruídas, inferidas a partir das configurações de poros, e associamos este resultado ao problema típico de casamento de impressões digitaisAbstract: The lack of robustness against the quality degradation affecting sets of features extracted from patterns of epidermal ridges on our fingers is one of the open issues in fingerprint image analysis, with implications for security, privacy, and identity fraud. In this doctorate work we introduce a new methodology to analyze the quality of sets of level-3 fingerprint features represented by pores. Our approach takes into account the spatial interrelationship between the considered features and some basic transformations involving point process and anisotropic analysis. We propose two new quality index algorithms, which have proved to be effective as a matcher predictor and in the definition of weights filtering out low-quality features from an identification process. To experimentally assess the performance of these algorithms and supply the absence of a feature-based controlled quality database in the biometric community, we created a dataset with features configurations containing different levels of quality. In this work, we also proposed a method for reconstructing phase images from a given set of pores coordinates. To validate this idea from an identification perspective, we considered the set of minutia present in the reconstructed images and inferred from the pores configurations and used this result in fingerprint matchingsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação01-P-3951/2011147050/2012-0CAPESCNP

Automatic classification of acquisition problems affecting fingerprint images in automated border controls

Automated Border Control (ABC) systems are technologies designed to increase the speed and accuracy of the identity verifications performed at international borders. A great number of ABCs deployed in different countries use fingerprint recognition techniques because of their high accuracy and user acceptability. However, the accuracy of fingerprint recognition methods can drastically decrease in this application context due to user-sensor interaction factors. This paper presents two main contributions. The first of them consists in an experimental evaluation performed to search the main negative aspects that could affect the usability and accuracy in ABCs based on fingerprint biometrics. The mainly considered aspects consists in the presence of luggage and cleanness of the finger skin. The second contribution consists in a novel approach for automatically identifying the type of user-sensor interaction that caused quality degradations in fingerprint samples. This method uses a specific feature set and computational intelligence techniques to detect non-idealities in the acquisition process and to suggest corrective actions to travelers and border guards. To the best of our knowledge, this is the first method in the literature designed to detect problems in user-sensor interaction different from improper pressures on the acquisition surface. We validated the proposed approach using a dataset of 2880 images simulating different scenarios typical of ABCs. Results shown that the proposed approach is feasible and can obtain satisfactory performance, with a classification error of 0.098

Strategies for intelligent interaction management and usability of biometric systems

Fingerprint biometric systems are one of the most popular biometric systems in current use, which takes a standard measure of a person's fingerprint to compare against the measure from an original stored template, which they have pre-acquired and associated with the known personal identification claimed by the user. Generally, the fingerprint biometric system consists of three stages including a data acquisition stage, a feature extraction stage and a matching extraction. This study will explore some essential limitations of an automatic fingerprint biometric system relating to the effects of capturing poor quality fingerprint images in a fingerprint biometric system and will investigate the interrelationship between the quality of a fingerprint image and other primary components of a fingerprint biometric system, such as the feature extraction operation and the matching process. In order to improve the overall performance of an automatic fingerprint biometric system, the study will investigate some possible ways to overcome these limitations. With the purpose of acquisition of an acceptable quality of fingerprint images, three components/enhancements are added into the traditional fingerprint recognition system in our proposed system. These are a fingerprint image enhancement algorithm, a fingerprint image quality evaluation algorithm and a feedback unit, the purpose of which is to provide analytical information collected at the image capture stage to the system user. In this thesis, all relevant information will be introduced, and we will also show some experimental results obtained with the proposed algorithms, and comparative studies with other existed algorithms will also be presented

Multi-resolution dental image registration based on genetic algorithm

The Automated Dental Identification System (ADIS) is a Post Mortem Dental Identification System. This thesis presents dental image registration, required for the preprocessing steps of the image comparison component of ADIS. We proposed a multi resolution dental image registration based on genetic algorithms. The main objective of this research is to develop techniques for registration of extracted subject regions of interest with corresponding reference regions of interest.;We investigated and implemented registration using two multi resolution techniques namely image sub sampling and wavelet decomposition. Multi resolution techniques help in the reduction of search data since initial registration is carried at lower levels and results are updated as the levels of resolutions increase. We adopted edges as image features that needed to be aligned. Affine transformations were selected to transform the subject dental region of interest to achieve better alignment with the reference region of interest. These transformations are known to capture complex image distortions. The similarity between subject and reference image has been computed using Oriented Hausdorff Similarity measure that is robust to severe noise and image degradations. A genetic algorithm was adopted to search for the best transformation parameters that give maximum similarity score.;Testing results show that the developed registration algorithm yielded reasonable results in accuracy for dental test cases that contained slight misalignments. The relative percentage errors between the known and estimated transformation parameters were less than 20% with a termination criterion of a ten minute time limit. Further research is needed for dental cases that contain high degree of misalignment, noise and distortions

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

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Multi scale ICA based iris recognition using BSIF and Hog

Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation (HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database image with test image final features to compute performance parameters. It is observed that the performance of the proposed method is better compared to existing methods

Gesture passwords: concepts, methods and challenges

Biometrics are a convenient alternative to traditional forms of access control such as passwords and pass-cards since they rely solely on user-specific traits. Unlike alphanumeric passwords, biometrics cannot be given or told to another person, and unlike pass-cards, are always “on-hand.” Perhaps the most well-known biometrics with these properties are: face, speech, iris, and gait. This dissertation proposes a new biometric modality: gestures. A gesture is a short body motion that contains static anatomical information and changing behavioral (dynamic) information. This work considers both full-body gestures such as a large wave of the arms, and hand gestures such as a subtle curl of the fingers and palm. For access control, a specific gesture can be selected as a “password” and used for identification and authentication of a user. If this particular motion were somehow compromised, a user could readily select a new motion as a “password,” effectively changing and renewing the behavioral aspect of the biometric. This thesis describes a novel framework for acquiring, representing, and evaluating gesture passwords for the purpose of general access control. The framework uses depth sensors, such as the Kinect, to record gesture information from which depth maps or pose features are estimated. First, various distance measures, such as the log-euclidean distance between feature covariance matrices and distances based on feature sequence alignment via dynamic time warping, are used to compare two gestures, and train a classifier to either authenticate or identify a user. In authentication, this framework yields an equal error rate on the order of 1-2% for body and hand gestures in non-adversarial scenarios. Next, through a novel decomposition of gestures into posture, build, and dynamic components, the relative importance of each component is studied. The dynamic portion of a gesture is shown to have the largest impact on biometric performance with its removal causing a significant increase in error. In addition, the effects of two types of threats are investigated: one due to self-induced degradations (personal effects and the passage of time) and the other due to spoof attacks. For body gestures, both spoof attacks (with only the dynamic component) and self-induced degradations increase the equal error rate as expected. Further, the benefits of adding additional sensor viewpoints to this modality are empirically evaluated. Finally, a novel framework that leverages deep convolutional neural networks for learning a user-specific “style” representation from a set of known gestures is proposed and compared to a similar representation for gesture recognition. This deep convolutional neural network yields significantly improved performance over prior methods. A byproduct of this work is the creation and release of multiple publicly available, user-centric (as opposed to gesture-centric) datasets based on both body and hand gestures