Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification using SIFT and RootSIFT Descriptors

Handwriting biometrics is the science of identifying the behavioural aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines Scale Invariant Feature Transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMM). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While a SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates a SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic and one hybrid language) and the results have shown the superiority of the proposed system over state-of-the-art techniques

Efficient Signatures Verification System Based on Artificial Neural Networks

Biometrics refer to the system of authenticating identities of humans, using features like retina scans, thumb and fingerprint scanning, face recognition and also signature recognition. Signatures are a simple and natural method of verifying a person’s identity. It can be saved as an image and verified by matching, using neural networks. Signature verification can be offline or online. In this work, we present a system for offline signature verification. The user has to submit a number of signatures that are used to extract two types of features, statistical features and structural features. A vector obtained from each of them is used to train propagation neural network in the verification stage. A test signature is then taken from the user, to compare it with those the network had been trained with. A test experiment was carried out with two sets of data. One set is used as a training set for the propagation neural network in its verification stage. This set with four signatures form each user is used for the training purpose. The second set consists of one sample of signature for each of the 20 persons is used as a test set for the system. A negative identification test was carried out using a signature of one person to test others’ signatures. The experimental results for the accuracy showed excellent false reject rate and false acceptance rate

Offline Signature Verification for Arabic Language

Biometrics relies on biological features (e.g. finger print, iris or the retina) or behavioral features (voice, signature). Those features can be used for identity verification for an individual. For this it became one of the most trusted and natural ways to identify a person and controlling access to the systems. Signature is a behavioral biometric. Signature is not unique like iris or finger print as it can be forged. Automatic signature verification is divided into two areas depending on the way of data capturing: offline and online signature verification. In offline signature verification, the signature is scanned from a document using a scanner to get the image of the signature. In online signature, a digitizing tablet is used to collect the movements during the signing. In this work we present a system for offline signature verification. In this system the user has to submit a number of signatures which are used to extract two types of features, statistical features and structural features. A vector obtained from each of them is used to train propagation neural net in the verification stage. A test signature is then taken from the user, to compare it with those the net had been trained with. A test experiment was carried out with two sets of data are collected. One set is used as a training set for the propagation neural net in its verification stage. This set with four signatures form each user is used for the training purpose. The second set consisting of one sample of signature for each of the 20 persons is used as a test set for the system. A negative identification test was carried out using a signature of one person to test others’ signatures. The system gave encouraging results

Evaluation methodologies for security testing biometric systems beyond technological evaluation

The main objective of this PhD Thesis is the specification of formal evaluation methodologies for testing the security level achieved by biometric systems when these are working under specific contour conditions. This analysis is conducted through the calculation of the basic technical biometric system performance and its possible variations. To that end, the next two relevant contributions have been developed. The first contribution is the definition of two independent biometric performance evaluation methodologies for analysing and quantifying the influence of environmental conditions and human factors respectively. From the very beginning it has been claimed and demonstrated that these two contour conditions are the most significant parameters that may affect negatively the biometric performance. Nevertheless, in spite of ISO/IEC 19795 standard [ISO'06b], which addresses biometric performance testing and reporting, being published in 2006, no evaluation methodology for assessing such adverse effects has been implemented yet. Therefore, this dissertation proposes both methodologies which have been defined in accordance to the following requirements: - should be general and modality independent for covering the analysis of all kind of biometric systems; - should conform to the principles and requirements already defined in ISO/IEC 19795 multipart standard; and - should provide requirements and procedures to accurately define the evaluation conditions to be tested, conduct reproducible test methods and obtain objective and intercomparable results. The second relevant contribution is the development of detailed guidelines for addressing how to conduct biometric performance evaluations in compliance with Common Criteria [CC]. Common Criteria is currently the only international recognised evaluation framework with which developers have to analyse and demonstrate the level of security achieved by their products. However, the applicability of this methodology to biometrics needs the specification of supplementary guidelines. As a consequence, this dissertation proposes such guidelines which have been specified according to the following requirements: - should be independent of any biometric modality; - should be based on previous works published in this topic BTSE [BTSE'01], BEM [BEM'02] and the ISO/IEC 19792 international standard which addresses security evaluation of biometric system; - should conform to the last version of both Common Criteria and the ISO/IEC 19795 multipart standards; and - should cover those kinds of biometric performance evaluations that can be repeatable, i.e. technology and scenario evaluations as well as the Common Criteria evaluation activities involved in the execution of such test procedures. As for the evaluation of the security of biometric systems there is the need of determine their performance, and as such performance also depends on contour conditions, both evaluation methodologies (i.e. environmental and human factors) and Common Criteria guidelines, are merged in order to provide improved evaluation methodology for the security of biometric systems. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------El objetivo principal de esta Tesis Doctoral es la especificación de metodologías de evaluación formales para analizar el nivel de seguridad alcanzado por los sistemas biométricos cuando estos se encuentran trabajando bajo condiciones de contorno específicas. Este análisis se realiza a través del cálculo del rendimiento técnico básico del sistema biométrico y sus posibles variaciones. A tal efecto, se han elaborado las siguientes contribuciones. En primer lugar, se han especificado dos metodologías de evaluación de rendimiento biométrico de manera independiente para analizar y cuantificar la influencia de las condiciones ambientales y los factores humanos, respectivamente. Desde los primeros estudios sobre rendimiento biométrico, se ha afirmado y demostrado que éstos son los parámetros más significativos que pueden afectar negativamente al rendimiento biométrico. No obstante, a pesar de que la norma ISO/IEC 19795 que regula la evaluación y documentación del rendimiento de los sistemas biométricos fue publicada en 2006, ninguna metodología que evalúe dichos efectos adversos ha sido implementada hasta el momento. Por lo tanto la presente Tesis Doctoral propone ambas metodologías, las cuáles han sido definidas conforme a las siguientes condiciones: - son de carácter general e independientes de cualquier modalidad biométrica para cubrir el análisis de todo tipo de sistemas biométricos, - cumplen con los principios y requisitos previamente definidos en la norma internacional ISO/IEC 19795 [ISO'06b], y - proporcionan requisitos y procedimientos detallados para: definir las condiciones de los ensayos, efectuar métodos de ensayo reproducibles y obtener resultados objetivos e intercomparables. En segundo lugar, se han desarrollado directrices específicas que abordan la forma de realizar evaluaciones de rendimiento biométrico conforme a "Common Criteria for IT security evaluation" (conocido habitualmente como "Common Criteria" [CC]). Common Criteria es actualmente el único marco de evaluación internacionalmente reconocido del que disponen los desarrolladores de sistemas biométricos para analizar y demostrar el nivel de seguridad que alcanzan sus productos. Sin embargo, la aplicación de esta metodología a la tecnología biométrica requiere la especificación de pautas complementarias. Por consiguiente, esta Tesis Doctoral propone tales pautas o directrices, las cuáles se han especificado de acuerdo con los siguientes requisitos: - son independientes de cualquier modalidad biométrica, - se basan en los trabajos previos que ya han sido publicados en esta área tales como BTSE [BTSE'01], BEM [BEM'02] y el estándar internacional ISO/IEC 19792 [ISO'09a] que regula la evaluación de seguridad de los sistemas biométricos, - son conformes a las últimas versiones tanto de Common Criteria como de la norma internacional ISO/IEC 19795, y - cubren tanto el tipo de evaluaciones de rendimiento biométrico que pueden ser repetibles, es decir las evaluaciones tecnológicas y de escenario, como las actividades de evaluación establecidas por la norma Common Criteria que conllevan la realización de dichos procedimientos de test. Debido a que es necesario determinar el rendimiento de los sistemas biométricos para evaluar su seguridad, y ya que dicho rendimiento depende de distintas condiciones de contorno, las dos metodologías de evaluación previamente definidas (condiciones ambientales y factores humanos) se han unido con las directrices de Common Criteria, para así conseguir una mejora sustancial en la metodología de evaluación de la seguridad de los sistemas biométricos

A Multi-modal Biometric System for Selective Access Control

The goal of this thesis is the design and the implementation of an adap- tive multi-modal biometric system with serial aquisition mode, intended to manage the accesses of structure, according to a predefined set of security levels and requirements, stated in a formal way in the SLA at a negotiation phase. In a multi-modal process multiple biometric traits are collected from the same individual, requiring different sensors. The chosen and combined traits are face and iris

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Advancing iris biometric technology

PhD ThesisThe iris biometric is a well-established technology which is already in use in several nation-scale applications and it is still an active research area with several unsolved problems. This work focuses on three key problems in iris biometrics namely: segmentation, protection and cross-matching. Three novel methods in each of these areas are proposed and analyzed thoroughly. In terms of iris segmentation, a novel iris segmentation method is designed based on a fusion of an expanding and a shrinking active contour by integrating a new pressure force within the Gradient Vector Flow (GVF) active contour model. In addition, a new method for closed eye detection is proposed. The experimental results on the CASIA V4, MMU2, UBIRIS V1 and UBIRIS V2 databases show that the proposed method achieves state-of-theart results in terms of segmentation accuracy and recognition performance while being computationally more efficient. In this context, improvements by 60.5%, 42% and 48.7% are achieved in segmentation accuracy for the CASIA V4, MMU2 and UBIRIS V1 databases, respectively. For the UBIRIS V2 database, a superior time reduction is reported (85.7%) while maintaining a similar accuracy. Similarly, considerable time improvements by 63.8%, 56.6% and 29.3% are achieved for the CASIA V4, MMU2 and UBIRIS V1 databases, respectively. With respect to iris biometric protection, a novel security architecture is designed to protect the integrity of iris images and templates using watermarking and Visual Cryptography (VC). Firstly, for protecting the iris image, text which carries personal information is embedded in the middle band frequency region of the iris image using a novel watermarking algorithm that randomly interchanges multiple middle band pairs of the Discrete Cosine Transform (DCT). Secondly, for iris template protection, VC is utilized to protect the iii iris template. In addition, the integrity of the stored template in the biometric smart card is guaranteed by using the hash signatures. The proposed method has a minimal effect on the iris recognition performance of only 3.6% and 4.9% for the CASIA V4 and UBIRIS V1 databases, respectively. In addition, the VC scheme is designed to be readily applied to protect any biometric binary template without any degradation to the recognition performance with a complexity of only O(N). As for cross-spectral matching, a framework is designed which is capable of matching iris images in different lighting conditions. The first method is designed to work with registered iris images where the key idea is to synthesize the corresponding Near Infra-Red (NIR) images from the Visible Light (VL) images using an Artificial Neural Network (ANN) while the second method is capable of working with unregistered iris images based on integrating the Gabor filter with different photometric normalization models and descriptors along with decision level fusion to achieve the cross-spectral matching. A significant improvement by 79.3% in cross-spectral matching performance is attained for the UTIRIS database. As for the PolyU database, the proposed verification method achieved an improvement by 83.9% in terms of NIR vs Red channel matching which confirms the efficiency of the proposed method. In summary, the most important open issues in exploiting the iris biometric are presented and novel methods to address these problems are proposed. Hence, this work will help to establish a more robust iris recognition system due to the development of an accurate segmentation method working for iris images taken under both the VL and NIR. In addition, the proposed protection scheme paves the way for a secure iris images and templates storage. Moreover, the proposed framework for cross-spectral matching will help to employ the iris biometric in several security applications such as surveillance at-a-distance and automated watch-list identification.Ministry of Higher Education and Scientific Research in Ira