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

Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation

Im ersten Teil dieser Arbeit wird Fingerwachstum untersucht und eine Methode zur Vorhersage von Wachstum wird vorgestellt. Die Effektivität dieser Methode wird mittels mehrerer Tests validiert. Vorverarbeitung von Fingerabdrucksbildern wird im zweiten Teil behandelt und neue Methoden zur Schätzung des Orientierungsfelds und der Ridge-Frequenz sowie zur Bildverbesserung werden vorgestellt: Die Line Sensor Methode zur Orientierungsfeldschätzung, gebogene Regionen zur Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur Bildverbesserung. Multi-level Jugdment Aggregation wird eingeführt als Design Prinzip zur Kombination mehrerer Methoden auf mehreren Verarbeitungsstufen. Schließlich wird Score Neubewertung vorgestellt, um Informationen aus der Vorverarbeitung mit in die Score Bildung einzubeziehen. Anhand eines Anwendungsbeispiels wird die Wirksamkeit dieses Ansatzes auf den verfügbaren FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the thesis and a method for growth prediction is presented. The effectiveness of the method is validated in several tests. Fingerprint image preprocessing is discussed in the second part and novel methods for orientation field estimation, ridge frequency estimation and image enhancement are proposed: the line sensor method for orientation estimation provides more robustness to noise than state of the art methods. Curved regions are proposed for improving the ridge frequency estimation and curved Gabor filters for image enhancement. The notion of multi-level judgment aggregation is introduced as a design principle for combining different methods at all levels of fingerprint image processing. Lastly, score revaluation is proposed for incorporating information obtained during preprocessing into the score, and thus amending the quality of the similarity measure at the final stage. A sample application combines all proposed methods of the second part and demonstrates the validity of the approach by achieving massive verification performance improvements in comparison to state of the art software on all available databases of the fingerprint verification competitions (FVC)

Documentation and analysis of plastic fingerprint impressions involving contactless three-dimensional surface scanning

Fingerprint impressions are frequently encountered during the investigation of crime scenes, and may establish a crucial linkage between the suspect and the crime scene. Plastic fingerprint impressions found at crime scenes are often transient and delicate, leaving photography the sole means of documentation. A traditional photography approach can be inadequate in documenting impressions that contain three-dimensional (3D) details due to the limitations of camera and lighting conditions on scene. In this study, 3D scanning was proposed as a novel method for the documentation of plastic fingerprints. Structured-light 3D scanning (SLS) captures the distortion of projected light patterns on the subject to obtain its 3D profile, which allows fast acquisition of the complete 3D geometric information of the surface. The contactless operation of SLS also eliminates the risk of destroying fragile evidence, making it a sound choice for forensic applications. In this study, the feasibility of 3D scanning of plastic fingerprint impressions was evaluated and compared with traditional photography regarding the quantity and quality of perceptible friction ridge features. Attempts were made to develop a procedure to extract curvature features from 3D scanned fingerprints and flatten the friction ridge features into two-dimensional (2D) images to allow direct comparison with the traditional photography method in the CSIpix® Matcher and NFIQ 2.0 software. One of the developed methods (3DR) utilizing a discrete geometry operator and convexity features outperformed traditional photography, both in minutiae count and match quality, while traditional photography could not always capture enough high-quality minutiae for comparisons, even after digital enhancement. The reproducibility of the 3D scanning process was evaluated using 3D point cloud statistics. The pair-wise mean distance and standard deviation were calculated for four levels of comparisons with theoretically increasing disparity, including pairs of scans of the same impressions. The results showed minimal shape deviation from scan to scan for the same impression, but high variations for different impressions

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

Reconstruction of fingerprints from minutiae points

Most fingerprint authentication systems utilize minutiae information to compare fingerprint images. During enrollment, the minutiae template of a user\u27s fingerprint is extracted and stored in the database. In this work, we concern ourselves with the amount of fingerprint information that can be elicited from the minutiae template of a user\u27s fingerprint. We demonstrate that minutiae information can reveal substantial details such as the orientation field and class of the (unseen) parent fingerprint that can potentially be used to reconstruct the original fingerprint image.;Given a minutiae template, the proposed method first estimates the orientation map of the parent fingerprint by constructing minutiae triplets. The estimated orientation map is observed to be remarkably consistent with the underlying ridge flow of the unseen parent fingerprint. We also discuss a fingerprint classification technique that utilizes only the minutiae information to determine the class of the fingerprint (Arch, Left loop, Right loop and Whorl). The proposed classifier utilizes various properties of the minutiae distribution such as angular histograms, density, relationship between minutiae pairs, etc. A classification accuracy of 82% is obtained on a subset of the NIST-4 database. This indicates that the seemingly random minutiae distribution of a fingerprint can reveal important class information. (Abstract shortened by UMI.)