383 research outputs found
HMM-based on-line signature verification: Feature extraction and signature modeling
This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 28.16 (2007): 2325 – 2334, DOI: 10.1016/j.patrec.2007.07.012A function-based approach to on-line signature verification is presented. The system
uses a set of time sequences and Hidden Markov Models (HMMs). Development
and evaluation experiments are reported on a subcorpus of the MCYT bimodal
biometric database comprising more than 7,000 signatures from 145 subjects. The
system is compared to other state-of-the-art systems based on the results of the
First International Signature Verification Competition (SVC 2004). A number of
practical findings related to feature extraction and modeling are obtained.This work has been supported by the Spanish projects TIC2003-08382-C05-
01 and TEC2006-13141-C03-03, and by the European NoE Biosecure
Efficient on-line signature verification system
In this paper, a robust automatic on-line signature
verification system is proposed. The effectiveness of any on-line
signature verification system depends mainly on the robustness
of the dynamic features use in the system. Inability to extract
highly discriminative dynamic features from signature has been
contributing to higher verification error-rates. On-line signature
verification experiments are conducted on seven dynamic
signature features extracted from signature trajectories. Three
features are found to be highly discriminative in comparison with
others. The proposed system incorporates these three features for
signature verification. Verification is based on the average of all
the distances obtain from the cross-alignment of the features. The
proposed system is tested with quality signature samples and it
has 0.5% error in rejecting skilled forgeries while rejecting only
0.25% of genuine signatures. These results are better in
comparison with the results obtained from previous systems
Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
We present a new approach for online handwritten signature classification and
verification based on descriptors stemming from Information Theory. The
proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher
Information evaluated over the Bandt and Pompe symbolization of the horizontal
and vertical coordinates of signatures. These six features are easy and fast to
compute, and they are the input to an One-Class Support Vector Machine
classifier. The results produced surpass state-of-the-art techniques that
employ higher-dimensional feature spaces which often require specialized
software and hardware. We assess the consistency of our proposal with respect
to the size of the training sample, and we also use it to classify the
signatures into meaningful groups.Comment: Submitted to PLOS On
Incorporating signature verification on handheld devices with user-dependent Hidden Markov Models
Proceedings of the International Conference on Frontiers in Hadwriting Recognition (ICFHR 2008)A dynamic signature verification system based on Hidden
Markov Models is presented. For each user model,
the number of states and Gaussian mixtures of the Hidden
Markov Model is automatically set in order to optimize
the verification performance. By introducing this userdependent
structure in the statistical modeling of signatures,
the system error rate is significantly decreased in
the challenging scenario of dynamic signature verification
on handheld devices. Experimental results are given on a
subset of the recently acquired BIOSECURE multimodal
database, using signatures captured with a PDAThis work has been supported by the Spanish Ministry of Education under project TEC2006-13141-C03-03
Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11
Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio
Online signature verification using hybrid wavelet transform
Online signature verification is a prominent behavioral biometric trait. It offers many dynamic features along with static two dimensional signature image. In this paper, the Hybrid Wavelet Transform (HWT) was generated using Kronecker product of two orthogonal transform such as DCT, DHT, Haar, Hadamard and Kekre. HWT has the ability to analyze the signal at global as well as local level like wavelet transform. HWT-1 and -2 was applied on the first 128 samples of the pressure parameter and first 16 samples of the output were used as feature vector for signature verification. This feature vector is given to Left to Right HMM classifier to identify the genuine and forged signature. For HWT-1, DCT HAAR offers best FAR and FRR. . For HWT-2, KEKRE 128 offers best FAR and FRR. HWT-1 offers better performance than HWT- 2 in terms of FAR and FRR. As the number of states increase, the performance of the system improves. For HWT - 1, KEKRE 128 offers best performance at 275 symbols whereas for HWT - 2, best performance is at 475 symbols by KEKRE 128
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
MULTI-MODEL BIOMETRICS AUTHENTICATION FRAMEWORK
Authentication is the process to conform the truth of an attribute claimed by real entity. Biometric technology is widely useful for the process of authentication. Today, biometric is becoming a key aspect in a multitude of applications. So this paper proposed the applications of such a multimodal biometric authentication system. Proposed system establishes a real time authentication framework using multi-model biometrics which consists of the embedded system verify the signatures, fingerprint and key pattern to authenticate the user. This is one of the most reliable, fast and cost effective tool for the user authentication
Sign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital Pen
Although in many cases contracts can be made or ended digitally, laws require handwritten signatures in certain cases. Forgeries are a major challenge with digital contracts, as their validity is not always immediately apparent without forensic methods. Illiteracy or disabilities may result in a person being unable to write their full name. In this case x-mark signatures are used, which require a witness for validity. In cases of suspected fraud, the relationship of the witnesses must be questioned, which involves a great amount of effort. In this paper we use audio and motion data from a digital pen to identify users via handwritten symbols. We evaluated the performance our approach for 19 symbols in a study with 30 participants. We found that x-marks offer fewer individual features than other symbols like arrows or circles. By training on three samples and averaging three predictions we reach a mean F1-score of F1 = 0.87, using statistical and spectral features fed into SVMs
- …