Although several automatic computer systems have been proposed to address facial expression recognition problems, the majority of them still fail to cope with some requirements of many practical application scenarios. In this paper, one of the most influential and common issues raised in practical application scenarios when applying automatic facial expression recognition system, head pose variation, is comprehensively explored and investigated. In order to do this, two novel texture feature representations are proposed for implementing multi-view facial expression recognition systems in practical environments. These representations combine the block-based techniques with Local Ternary Pattern-based features, providing a more informative and efficient feature representation of the facial images. In addition, an in-house multi-view facial expression database has been designed and collected to allow us to conduct a detailed research study of the effect of out-of-plane pose angles on the performance of a multi-view facial expression recognition system. Along with the proposed in-house dataset, the proposed system is tested on two well-known facial expression databases, CK+ and BU-3DFE datasets. The obtained results shows that the proposed system outperforms current state-of-the-art 2D facial expression systems in the presence of pose variations.

In the context of facial expression recognition (FER), this paper reviews the fundamental theories of emotions and further explains the key dimensions of a defined emotional space. The main contribution of this paper is to propose a set of novel categorization methods for facial expressions to be used in the design of an automatic FER system. This novel categorization enables the facial expression to be interpreted in a better way that and to be more effective in practical applications of automatic FER systems. In order to validate the feasibility of the proposed categorization methods, a set of experiments is reported which investigates and analyzes the influence that the novel categorization brings to a multi-view FER system.

“Natural revocability” is an extremely simple and intuitive strategy for the revocation process in the event of compromise without the need for complex mathematical processing, This paper investigates the possibilities of adopting the strategy, in relation to security and reliability in handwritten signature analysis by exploring the effect of writer style, age and gender on signature analysis. This is examined by performing analysis of variance for the extracted features based on three style categories, two gender groups and three age groups. The results from the analysis provide some valuable insight into the concept of natural revocability as a function of writer style, age and gender.

The use of soft-biometric data as an auxiliary tool on user identification is already well known. Gender, handorientation and emotional state are some examples which can be called soft-biometrics. These soft-biometric data can be predicted directly from the biometric templates. It is very common to find researches using physiological modalities for soft-biometric prediction, but behavioural biometric is often not well explored for this context. Among the behavioural biometric modalities, keystroke dynamics and handwriting signature have been widely explored for user identification, including some soft-biometric predictions. However, in these modalities, the soft-biometric prediction is usually done in an individual way. In order to fill this space, this study aims to investigate whether the combination of those two biometric modalities can impact the performance of a soft-biometric data, gender prediction. The main aim is to assess the impact of combining data from two different biometric sources in gender prediction. Our findings indicated gains in terms of performance for gender prediction when combining these two biometric modalities, when compared to the individual ones.

‘Enhancing biometric processing’ explores how the field of biometrics is developing, and the main ideas promoting the improvement of the accuracy, reliability, and effectiveness of biometric systems. It is unlikely that any individual biometric modality operating alone will completely meet all the desirable criteria for a given task, especially when the variety of issues that are needed are considered in any practical situation. To improve system performance different systems can be used such as adding extra power using a multiclassifier configuration, increasing flexibility using multimodal systems, and using soft biometrics as additional identity evidence. Resistance to ‘spoofing’ attacks, biometric data integrity, and extending the application domains for biometrics-based processing are also considered.

‘Biometrics: where should I start?’ considers what constitutes a practical biometric system. It looks at the principles on which such a system operates, builds up a picture of the components needed to construct such a system, and takes the first steps towards understanding how to implement a biometric system. It also looks at how, when, and why errors can arise and how the performance of a biometric system can be evaluated, both qualitatively and quantitatively, to understand more clearly the nature of the interaction between the user and the system itself, and to determine helpful ways of describing both basic system factors and user characteristics, which will ultimately influence system performance.

Facial expressions can be seen as a form of non-verbal communication as well as a primary means of conveying social information among humans.Automatic facial expression recognition (FER) can be applied to a wide range of scenarios in human-computer interaction, facial animation, entertainment, and psychology studies. For feature representation in a FER system, various texture descriptors have been employed to derive an effective solution for this system. However, these individual texture descriptor-based FER systems have often failed to achieve effective performance in the recognition of facial expressions. In this sense, it is necessary to further improve the general performance of a facial expression recognition system, evaluating different feature representations. In this paper, a novel local descriptor for a facial expression recognition system is proposed, designated the level of difference descriptor (LOD). The main goal is to use this descriptor as a supplement to state-of-the-art local descriptors to further improve the performance of a FER system in terms of classification accuracy. Furthermore, the fusion of various texture features for devising a robust feature representation for multi-view facial expression recognition is presented.

In order to assess the practicality or suitability of a particular biometric modality in any given application, or to judge how effective biometrics-based identity monitoring is likely to be in a particular situation, we need to understand the principal issues that affect a chosen modality. ‘Making biometrics work’ examines the relevant technologies of four major modalities—fingerprints, iris patterns, facial images, and the handwritten signature. All modalities offer both advantages and disadvantages, and performance will depend very much on the nature of the proposed application, the nature of the population who will be the primary users of the system, and the environment in which it is to operate.

Is there any reason why the data captured in a biometric system could not be used for other sorts of prediction too? ‘An introduction to predictive biometrics’ looks at how instead of supplementing conventional data with soft biometric data, conventional biometric data might be used to predict some soft biometric characteristics of an individual. Predictive biometrics has expanded considerably in recent years, with the two most frequently studied characteristics being age and gender using, for example, biometric iris data. Recent studies are taking predictive biometrics further still, aiming to predict so-called ‘higher level’ individual characteristics, such as those which reflect an individual’s mental or emotional state.

An off-line text-independent writer verification system that leverages the similarities with the field of speaker recognition by employing analogous techniques for modelling and comparing the features extracted from the input text images is presented. The main contribution of this work is the use of the i-vector paradigm in a writer verification setting. The proposed system is evaluated with images of lines of text from the IAM Handwriting Database, and compared with more traditional approaches. The authors also analyse several algorithms for the detection and extraction of points of interest in the text images, different parameters for the modelling part and different scoring techniques. The obtained results show that the use of i-vectors clearly improves the performance of the system even for configurations where the overhead of the additional calculations is minimal.

Iris-based biometric technologies have been widely adopted in recent years and implemented in many different scenarios. This has meant that increasing flexibility is now being sought in terms of how systems are configured to improve user-system interaction, while also producing a developing opportunity for applications in forensic analysis. Current typical iris biometric deployments, while generally expected to perform well, require a considerable level of co-operation from the system user. Specifically, the physical positioning of the human eye in relation to the iris capture device is a critical factor, which can substantially affect the performance of the overall iris biometric system. The work reported in this study will explore the acquisition of iris images captured at varying positions with respect to the capture device, and in particular presents a preliminary investigation of types of iris images captured when the gaze angle of a subject is not aligned with the axis of the camera lens.

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future.

As biometrics-based identity authentication systems have become more widely deployed, it has become evident that traditional identification and verification tasks are not the only application for such approaches. The prediction of individual, but non-unique, characteristics such as subject age is also an obvious option, since there are diverse situations in which information short of absolute identity is itself valuable. 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. Age prediction is relatively poorly represented in the literature. This chapter will explore applications of age prediction from iris biometrics and the implications for the underpinning computational structures.

The analysis of handwritten signatures is an area of ongoing and increasing interest for both biometrics and forensics researchers. Hesitation in writing execution, estimated (to a large degree) through human inspection, has been commonly used by Forensic Document Examiners (FDE) or Forensic Handwriting Experts (FHE) as one of the important features which may be used to distinguish between genuine signatures and imitations or forgeries. This paper introduces some simple objective measures of hesitation and investigates their reliability and efficacy in automating the analysis of handwritten signatures for forensic or biometric applications.

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.

This paper introduces an empirical investigation which directly addresses and explores gender prediction capacity from digitised handwriting data from several different perspectives - such as feature type (static/dynamic) and content (fixed/variable) types - in order to provide extensive experimental evidence and analysis to guide the development of a better understanding of the opportunities for and practical consequences of gender prediction from digital handwriting data.

Biometrics usually provides several advantages over traditional forms of identity authentication. However, there are some concerns about the security of personal biometric data, while these systems need to ensure their integrity and public acceptance. The idea of cancellable biometrics has been introduced to overcome these security concerns. In addition, an important way to increase security and performance in biometric systems is the multi-biometric approach, which combines different sources of biometric information. This paper investigates the benefits that optimized ensemble systems and cancellable transformation functions can provide to multibiometric authentication systems. The main aim of the work reported here is to provide more security and better performance in the multi-biometric authentication process. We use as examples two different biometric modalities (face and voice) separately and in the multi-modal context (multi-biometric). The datasets used in this analysis were TIMIT for voice and the AR Face dataset for Face. As a result of this analysis, we will observe that the use of cancellable transformations in the multi-biometric dataset increases the accuracy level for the ensemble systems. Abstract

As biometric systems are deployed in increasingly diverse applications, it becomes correspondingly important to understand the impact which human aging has on system performance. Aging directly affects those physiological and behavioral traits which are characterized in biometric measurements, and a practical biometric system must be designed to account for age-induced changes. However, age can also have very positive implications, for example as a source of further identification information. This paper reviews research to understand how age factors impinge on biometric systems and uses this to synthesize a system infrastructure to unify implementation principles. We present new results to show how multiagent structures can provide an effective framework for this purpose, enhancing performance in both identification and predictive scenarios.

The technologies of biometrics provide a variety of powerful tools to determine or confirm individual identity while, more recently, there has been considerable interest in using soft biometrics (personal information which is characteristic of, but not unique to, individuals) in the identification task. Increasingly, however, work has been developing to predict soft biometric information, such as predicting the age or gender of a subject, and this sort of process is clearly of particular interest in the context of criminal investigations. In this paper, we report some initial work to investigate the prediction of ”higher level” characteristics, specifically emotional state, of an individual from basic biometric data obtained from keystroke dynamics. We focus on the issue of specifying an underpinning computational platform based on a multiclassifier configuration and interacting agents to achieve better predictive performance than can be obtained using more traditional structures.

Prediction of gender characteristics from iris images has been investigated and some successful results have been reported in the literature, but without considering performance for different iris features and classifiers.This paper investigates for the first time an approach to gender prediction from iris images using different types of features (including a small number of very simple geometric features, texture features and a combination of geometric and texture features) and a more versatile and intelligent classifier structure.Our proposed approaches can achieve gender prediction accuracies of up to 90% in the BioSecure Database.