477 research outputs found
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information
Forensics and Securit
DeepSign: Deep On-Line Signature Verification
Deep learning has become a breathtaking technology in the last years,
overcoming traditional handcrafted approaches and even humans for many
different tasks. However, in some tasks, such as the verification of
handwritten signatures, the amount of publicly available data is scarce, what
makes difficult to test the real limits of deep learning. In addition to the
lack of public data, it is not easy to evaluate the improvements of novel
proposed approaches as different databases and experimental protocols are
usually considered.
The main contributions of this study are: i) we provide an in-depth analysis
of state-of-the-art deep learning approaches for on-line signature
verification, ii) we present and describe the new DeepSignDB on-line
handwritten signature biometric public database, iii) we propose a standard
experimental protocol and benchmark to be used for the research community in
order to perform a fair comparison of novel approaches with the state of the
art, and iv) we adapt and evaluate our recent deep learning approach named
Time-Aligned Recurrent Neural Networks (TA-RNNs) for the task of on-line
handwritten signature verification. This approach combines the potential of
Dynamic Time Warping and Recurrent Neural Networks to train more robust systems
against forgeries. Our proposed TA-RNN system outperforms the state of the art,
achieving results even below 2.0% EER when considering skilled forgery
impostors and just one training signature per user
Introduction to Presentation Attacks in Signature Biometrics and Recent Advances
Applications based on biometric authentication have received a lot of
interest in the last years due to the breathtaking results obtained using
personal traits such as face or fingerprint. However, it is important not to
forget that these biometric systems have to withstand different types of
possible attacks. This chapter carries out an analysis of different
Presentation Attack (PA) scenarios for on-line handwritten signature
verification. The main contributions of this chapter are: i) an updated
overview of representative methods for Presentation Attack Detection (PAD) in
signature biometrics; ii) a description of the different levels of PAs existing
in on-line signature verification regarding the amount of information available
to the impostor, as well as the training, effort, and ability to perform the
forgeries; and iii) an evaluation of the system performance in signature
biometrics under different scenarios considering recent publicly available
signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with
recent efforts in the Common Criteria standardization community towards
security evaluation of biometric systems.Comment: Chapter of the Handbook of Biometric Anti-Spoofing (Third Edition
An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach
Abstract: New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique
Distributed and biometric signature-based identity proofing system for the maritime sector
The maritime sector is an industry that faces significant and various challenges related to cyber security and data management, such as fraud and user authentication. Therefore, there is a need for a secure solution that can effectively manage data transactions while resolving digital identity. A biometric signature application in blockchain for fighting fraud and fake identities may provide a solution in the maritime sector. This research proposes a biometric signature and an IPFS network-blockchain framework to address these challenges. This paper also discusses the proposed framework's cyber security challenges that threaten behavioral biometric security
Multi-Modal Biometrics: Applications, Strategies and Operations
The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented
Combating shoulder-surfing: a hidden button gesture based scheme
This project describes an authentication technique that is shoulder-surfing
resistant. Shoulder surfing is an attack in which an attacker can get access to
private information by observing the user’s interaction with a terminal, or by
using recording tools to record the user interaction and study the obtained
data, with the objective of obtaining unauthorized access to a target user’s
personal information. The technique described here relies on gestural analysis
coupled with a secondary channel of authentication that uses button pressing.
The thesis presents and evaluates multiple alternative algorithms for gesture
analysis, and furthermore assesses the effectiveness of the technique.Universidade da Madeir
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