Automatic Handwriting Feature Extraction, Analysis and Visualization in the Context of Digital Palaeography

Digital palaeography is an emerging research area which aims to introduce digital image processing techniques into palaeographic analysis for the purpose of providing objective quantitative measurements. This paper explores the use of a fully automated handwriting feature extraction, visualization, and analysis system for digital palaeography which bridges the gap between traditional and digital palaeography in terms of the deployment of feature extraction techniques and handwriting metrics. We propose the application of a set of features, more closely related to conventional palaeographic assesment metrics than those commonly adopted in automatic writer identification. These features are emprically tested on two datasets in order to assess their effectiveness for automatic writer identification and aid attribution of individual handwriting characteristics in historical manuscripts. Finally, we introduce tools to support visualization of the extracted features in a comparative way, showing how they can best be exploited in the implementation of a content-based image retrieval (CBIR) system for digital archiving. Read More: http://www.worldscientific.com/doi/abs/10.1142/S021800141653001

Managing ageing effects in biometric systems

Biometric systems deal with the problem of determining or authenticating the identity of individuals based on measurements of their physiological or behavioural characteristics. However, these characteristics are likely to change with the natural ageing process (passage of time) and, as a result, developing biometric applications for long-term use becomes a particularly challenging task. Thus, increasingly, an understanding of the ageing process is becoming an important issue in terms of ensuring reliability in the face of changing data scenarios in biometric systems. In fact, both physical ageing (the effects of ageing across the whole ageing cycle on the biometric measurements used in the identification process) and template ageing (the effects of ageing across the time elapsed between enrolment and authentication) are important in a pmcticaJ context, and while these are obviously related phenomena, they are not the same thing, since template ageing also depends to some extent on the physical age of the subject within the relevant time frame. This thesis will describe a study to explore some important issues related to physical ageing issues of particular importance in biometric systems, in order to provide an improved understanding of the ageing problem which might be able to have a positive influence on the design, deployment and management of ageing issues in future biometric systems. We will explore and present quantitatively the results of a detaiJed investigation into the physical effects of ageing, will discuss the relationships between physical ageing and interrelated physical factors which have a bearing on how the impact of ageing can most effectively be investigated and understood, and will show how these factors can be manipulated in order to guide practical implementation towards achieving more reliable performance. We study two established and widely used biometric modalities, iris and signature, to provide a practical environment for experimentation and analysis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evaluating iris segmentation for scenario optimisation

Iris recognition is a biometric modality which offers the potential for high accuracy and, increasingly, for application in more diverse environments than hitherto. Poor segmentation is one of the most important factors likely to compromise iris recognition performance. Hence, research in the area of iris biometrics has often been focused on efforts to enhance the performance of iris segmentation techniques, and this has led to considerable work on iris segmentation. This paper presents a detailed investigation, evaluation and comparison of several segmentation approaches (including a new algorithm proposed by the authors) proposed in the literature based on their accuracy and processing speed. To be consistent with the research of others, for all quantitative experiments, algorithms have been evaluated on two iris databases, namely CASIA V1.0 and a subset of the BioSecure databas

Framework for managing ageing effects in signature biometrics

This paper investigates and explores the impact of physical ageing in signature biometrics. Experimental performance evaluation, using three different signature databases, is carried out to provide some new insights into the relationship between different practical factors, in particular clarifying the impact on recognition performance of the data collection protocols used and the use of the feature pools underpinning the signature processing. This analysis provides an alternative perspective from which to explore and manage physical ageing effects in signature biometrics. The paper demonstrates that the proposed strategy maximises system accuracy while minimising the performance differential across a population which is heterogeneous with respect to age, and across different databases. The results presented suggest that adoption of the strategy proposed can render a template update procedure less critical than hitherto expected

Analysis of Physical Ageing Effects in Iris Biometrics

Physical ageing is an important issue for practical biometrics, since it is known that the associated physiological changes can impair performance for most modalities. Understanding the effects of ageing is necessary, therefore both to optimise attainable performance but also to understand how to manage biometric templates, especially as the time elapsed between enrolment and use increases. Many factors can affect the performance of an iris recognition system, and in this study the authors report an experimental investigation of the interrelationship between some characteristics which are particularly relevant in understanding how to manage the practical effects of ageing with respect to this modality. Moreover, template ageing will itself be affected by issues related to the biological age of a subject, and this is also explored here

Towards the development of user tools for knowledge acquisition in digital document analysis

Handwritten documents provide a rich source of data and, with the growth in the availability of digitised documents, it becomes increasingly important to improve our ability to analyse and extract “knowledge” from such sources. This paper describes an approach to the provision of tools which can extract information about the writer of handwritten documents, especially those which were written in earlier times and which constitute key elements in our heritage and culture. We show how the constraints inherent in such documents influence our analytical approach, and we also show how developing appropriate “knowledge extraction” techniques can also be essential in other, more general, important application scenarios

A methodological framework for investigating age factors on the performance of biometric systems

Any individual's biometric data are likely to change with the passage of time and, as a result, developing biometric applications for long-term use is a challenging task. One of the factors which increases the challenge of dealing with ageing effects in biometric systems is that the age of an individual is a continuous variable, and it is impossible to investigate and understand ageing issues in biometric systems other than using discrete age "bands". However, this division of a given population into age-bands has generally been a rather arbitrary exercise, making it difficult to optimise or compare results in different studies. In this paper, we will investigate, document and analyse the effects of age-band assignment, improving our understanding of how to manage age-related data and pointing to the possibility of more objectively determining optimal age-bands which offer a greater possibility of minimizing the sensitivity of a system which relies on such information. We study specifically the iris (physiological) and signature (behavioural) modalities

Age prediction from iris biometrics

This paper proposes and investigates experimentally an approach to age prediction from iris images by using a combination of a small number of very simple geometric features, and a more versatile and intelligent classifier structure which can achieve accuracies to 75%. To our knowledge, this is the first experimental study of three class age prediction from iris images