Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

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

“Nonsense Rides Piggyback on Sensible Things”: The Past, Present, and Future of Graphology

“Nonsense rides piggyback on sensible things”, declares professional sceptic and questioned-document analyst Joe Nickell concerning graphology. This chapter examines graphology’s enduring allure and reach, despite its controversies, and considers its relationship with other types of handwriting analysis. It first asks: is it possible to metaphorically “dissect” the page of handwritten texts, to scrutinize writing as a “medical paratext” rich in information about the writer’s state of health? It then interrogates the nature of the connection between physical and mental states and handwriting. It demonstrates how academics are going “back to basics” with their enquiries into individual difference and handwriting features, and how digital methodologies are contributing to this. Thus, this chapter is an updated study of graphology, providing a wider understanding of the concept of the paratext by considering the information captured in handwriting in the context of a digital age

Chapter 9 “Nonsense Rides Piggyback on Sensible Things”

“Nonsense rides piggyback on sensible things”, declares professional sceptic and questioned-document analyst Joe Nickell concerning graphology. This chapter examines graphology’s enduring allure and reach, despite its controversies, and considers its relationship with other types of handwriting analysis. It first asks: is it possible to metaphorically “dissect” the page of handwritten texts, to scrutinize writing as a “medical paratext” rich in information about the writer’s state of health? It then interrogates the nature of the connection between physical and mental states and handwriting. It demonstrates how academics are going “back to basics” with their enquiries into individual difference and handwriting features, and how digital methodologies are contributing to this. Thus, this chapter is an updated study of graphology, providing a wider understanding of the concept of the paratext by considering the information captured in handwriting in the context of a digital age

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication

Handwriting authentication is a valuable tool used in various fields, such as fraud prevention and cultural heritage protection. However, it remains a challenging task due to the complex features, severe damage, and lack of supervision. In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address these issues. It can dynamically learn complex yet important features and accurately predict writer identities. Specifically, to remove the negative effects of imperfections and redundancy, we design an information-theoretic filter for pre-processing and propose a novel adaptive matching scheme to represent images as patches of local regions dominated by more important features. Through online optimization at inference time, the most informative patch embeddings are identified as the "most important" elements. Furthermore, we employ contrastive self-supervised training with a momentum-based paradigm to learn more general statistical structures of handwritten data without supervision. We conduct extensive experiments on five benchmark datasets and our manually annotated dataset EN-HA, which demonstrate the superiority of our CSSL-RHA compared to baselines. Additionally, we show that our proposed model can still effectively achieve authentication even under abnormal circumstances, such as data falsification and corruption.Comment: 10 pages, 4 figures, 3 tables, submitted to ACM MM 202

A Multi-Feature Selection Approach for Gender Identification of Handwriting based on Kernel Mutual Information

This paper presents a new flexible approach to predict the gender of the writers from their handwriting samples. Handwriting features like slant, curvature, line separation, chain code, character shapes, and more, can be extracted from different methods. Therefore, the multi-feature sets are irrelevant and redundant. The conflict of the features exists in the sets, which affects the accuracy of classification and the computing cost. This paper proposes an approach, named Kernel Mutual Information (KMI), that focuses on feature selection. The KMI approach can decrease redundancies and conflicts. In addition, it extracts an optimal subset of features from the writing samples produced by male and female writers. To ensure that KMI can apply the various features, this paper describes the handwriting segmentation and handwritten text recognition technology used. The classification is carried out using a Support Vector Machine (SVM) on two databases. The first database comes from the ICDAR 2013 competition on gender prediction, which provides the samples in both Arabic and English. The other database contains the Registration-Document-Form (RDF) database in Chinese. The proposed and compared methods were evaluated on both databases. Results from the methods highlight the importance of feature selection for gender prediction from handwriting

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Contributions to non-conventional biometric systems : improvements on the fingerprint, facial and handwriting recognition approach

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2021.Os sistemas biométricos são amplamente utilizados pela sociedade. A maioria das aplicações desses sistemas está associada à identificação civil e à investigação criminal. No entanto, com o tempo, o desempenho dos métodos tradicionais de biometria está chegando ao limite. Neste contexto, sistemas biométricos emergentes ou não convencionais estão ganhando importância. Embora promissores, novos sistemas, assim como qualquer nova tecnologia, trazem consigo não apenas potencialidades, mas também fragilidades. Este trabalho apresenta contribuições para três importantes sistemas biométricos não convencionais (SBNC): impressão digital, reconhecimento facial e reconhecimento de escrita. No que diz respeito às impressões digitais, este trabalho apresenta um novo método para detectar a vida em dispositivos de impressão digital multivista sem toque, utilizando descritores de textura e redes neurais artificiais. Com relação ao reconhecimento facial, um método de reconhecimento de faces baseado em algoritmos de característica invariante à escala (SIFT e SURF) que opera sem a necessidade de treinamento prévio do classificador e que realiza o rastreamento de indivíduos em ambientes não controlados é apresentado. Finalmente, um método de baixo custo que usa sinais de acelerômetro e giroscópio obtidos a partir de um sensor acoplado a canetas convencionais para realizar o reconhecimento em tempo real de assinaturas é apresentado. Resultados mostram que os métodos propostos são promissores e que juntos podem contribuir para o aprimoramento dos SBNCCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Biometric systems are widely used by society. Most applications are associated with civil identification and criminal investigation. However, over time, traditional methods of performing biometrics have been reaching their limits. In this context, emerging or nonconventional biometric systems (NCBS) are gaining ground. Although promising, new systems, as well as any new technology, bring not only potentialities but also weaknesses. This work presents contributions to three important non-conventional biometric systems: fingerprint, facial, and handwriting recognition. With regard to fingerprints, this work presents a novel method for detecting life on Touchless Multi-view Fingerprint Devices, using Texture Descriptors and Artificial Neural Networks. With regard to face recognition, a facial recognition method is presented, based on Scale Invariant Feature Algorithms (SIFT and SURF), that operates without the need of previous training of a classifier and can be used to track individuals in an unconstrained environment. Finally, a low-cost on-line handwriting signature recognition method that uses accelerometer and gyroscope signals obtained from a sensor coupled to conventional pens to identify individuals in real time is presented. Results show that the proposed methods are promising and that together may contribute to the improvement of the NCB