161 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
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
Offline signature verification using classifier combination of HOG and LBP features
We present an offline signature verification system based on a signature’s local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are
calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP). The classification is performed using Support Vector Machines (SVMs), where two different approaches for training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s signature from others, whereas a single global SVM trained with difference vectors
of query and reference signatures’ features of all users, learns how to weight dissimilarities. The global SVM classifier is trained using genuine and forgery signatures of subjects that are excluded from the test set, while userdependent
SVMs are separately trained for each subject using genuine and random forgeries.
The fusion of all classifiers (global and user-dependent classifiers trained with each feature type), achieves a 15.41% equal error rate in skilled forgery test, in the GPDS-160 signature database without using any skilled forgeries
in training
BioTouchPass: Handwritten Passwords for Touchscreen Biometrics
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibleThis work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One-
Time Passwords (OTP) through the incorporation of biometric information as a second level of user authentication. In our
proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual. A
complete analysis of our proposed biometric system is carried out regarding the discriminative power of each handwritten digit and
the robustness when increasing the length of the password and the number of enrolment samples. The new e-BioDigit database,
which comprises on-line handwritten digits from 0 to 9, has been acquired using the finger as input on a mobile device. This
database is used in the experiments reported in this work and it is available together with benchmark results in GitHub1. Finally,
we discuss specific details for the deployment of our proposed approach on current PIN and OTP systems, achieving results with
Equal Error Rates (EERs) ca. 4.0% when the attacker knows the password. These results encourage the deployment of our
proposed approach in comparison to traditional PIN and OTP systems where the attack would have 100% success rate under
the same impostor scenarioThis work has been supported by projects: BIBECA (MINECO), Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación CientÃfica 2017) and by UAM-CecaBank.
Ruben Tolosana is supported by a FPU Fellowship from Spanish MEC
Deep Attentive Time Warping
Similarity measures for time series are important problems for time series
classification. To handle the nonlinear time distortions, Dynamic Time Warping
(DTW) has been widely used. However, DTW is not learnable and suffers from a
trade-off between robustness against time distortion and discriminative power.
In this paper, we propose a neural network model for task-adaptive time
warping. Specifically, we use the attention model, called the bipartite
attention model, to develop an explicit time warping mechanism with greater
distortion invariance. Unlike other learnable models using DTW for warping, our
model predicts all local correspondences between two time series and is trained
based on metric learning, which enables it to learn the optimal data-dependent
warping for the target task. We also propose to induce pre-training of our
model by DTW to improve the discriminative power. Extensive experiments
demonstrate the superior effectiveness of our model over DTW and its
state-of-the-art performance in online signature verification.Comment: Accepted at Pattern Recognitio
Variation Detection applied in User Signature Verification
Behavior studies have been conducted by scientists and philosophers who approach subjects such as star and planet trajectories, society organizations, living beings evolution and human language. With the advent of computer, new challenges have been observed in order to explore and understand the behavior variations of interactions with systems. Motivated by those challenges, this work proposes a new approach to automatically cluster, detect and identify behavior patterns. In order to validate this approach, we have modeled the knowledge embedded in interactions of handwriting signatures. The generated knowledge models were, afterwards, employed to verify signatures. Obtained results were compared to other related approaches presented in SVC2004, the First International Signature Verification Competition
Increasing the Robustness of Biometric Templates for Dynamic Signature Biometric Systems
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. R. Tolosana, R. Vera-Rodriguez, J. Ortega-Garcia and J. Fierrez, "Increasing the robustness of biometric templates for dynamic signature biometric systems," Security Technology (ICCST), 2015 International Carnahan Conference on, Taipei, 2015, pp. 229-234. doi: 10.1109/CCST.2015.7389687Due to the high deployment of devices such as
smartphones and tablets and their increasing popularity in our
society, the use of biometric traits in commercial and banking
applications through these novel devices as an easy, quick and
reliable way to perform payments is rapidly increasing. The
handwritten signature is one of the most socially accepted
biometric traits in these sectors due to the fact that it has been
used in financial and legal transitions for centuries. In this paper
we focus on dynamic signature verification systems. Nowadays,
most of the state-of-the-art systems are based on extracting
information contained in the X and Y spatial position coordinates
of the signing process, which is stored in the biometric templates.
However, it is critical to protect this sensible information of
the users signatures against possible external attacks that would
allow criminals to perform direct attacks to a biometric system
or carry out high quality forgeries of the users signatures.
Following this problem, the goal of this work is to study
the performance of the system in two cases: first, an optimal
time functions-based system taking into account the information
related to X and Y coordinates and pressure, which is the common
practice (i.e. Standard System). Second, we study an extreme
case not considering information related to X, Y coordinates and
their derivatives on the biometric system (i.e. Secure System),
which would be a much more robust system against attacks,
as this critical information would not be stored anywhere. The
experimental work is carried out using e-BioSign database which
makes use of 5 devices in total. The systems considered in this
work are based on Dynamic Time Warping (DTW), an elastic
measure over the selected time functions. Sequential Forward
Features Selection (SFFS) is applied as a reliable way to obtain
an optimal time functions vector over a development subset of
users of the database. The results obtained over the evaluation
subset of users of the database show a similar performance for
both Standard and Secure Systems. Therefore, the use of a Secure
System can be useful in some applications such as banking in
order to avoid the lost of important user information against
possible external attacks.This work was supported in part by the Project Bio-Shield (TEC2012-34881), in part by Cecabank e-BioFirma Contract, in part by the BEAT Project (FP7-SEC-284989) and in part by Catedra UAM-Telefonica
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