81 research outputs found
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
Biometric Signature Verification Using Recurrent Neural Networks
“© 2017 IEEE. 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.”Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-The art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeriesThis work has been supported by project TEC2015-70627-R MINECO/FEDER and by UAM-CecaBank Project. Ruben Tolosana is supported by a FPU Fellowship from Spanish MEC
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 Científica 2017) and by UAM-CecaBank.
Ruben Tolosana is supported by a FPU Fellowship from Spanish MEC
Offline Text-Independent Writer Identification based on word level data
This paper proposes a novel scheme to identify the authorship of a document
based on handwritten input word images of an individual. Our approach is
text-independent and does not place any restrictions on the size of the input
word images under consideration. To begin with, we employ the SIFT algorithm to
extract multiple key points at various levels of abstraction (comprising
allograph, character, or combination of characters). These key points are then
passed through a trained CNN network to generate feature maps corresponding to
a convolution layer. However, owing to the scale corresponding to the SIFT key
points, the size of a generated feature map may differ. As an alleviation to
this issue, the histogram of gradients is applied on the feature map to produce
a fixed representation. Typically, in a CNN, the number of filters of each
convolution block increase depending on the depth of the network. Thus,
extracting histogram features for each of the convolution feature map increase
the dimension as well as the computational load. To address this aspect, we use
an entropy-based method to learn the weights of the feature maps of a
particular CNN layer during the training phase of our algorithm. The efficacy
of our proposed system has been demonstrated on two publicly available
databases namely CVL and IAM. We empirically show that the results obtained are
promising when compared with previous works
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