55,826 research outputs found
Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach
Offline Signature Verification (OSV) is a challenging pattern recognition
task, especially when it is expected to generalize well on the skilled
forgeries that are not available during the training. Its challenges also
include small training sample and large intra-class variations. Considering the
limitations, we suggest a novel transfer learning approach from Persian
handwriting domain to multi-language OSV domain. We train two Residual CNNs on
the source domain separately based on two different tasks of word
classification and writer identification. Since identifying a person signature
resembles identifying ones handwriting, it seems perfectly convenient to use
handwriting for the feature learning phase. The learned representation on the
more varied and plentiful handwriting dataset can compensate for the lack of
training data in the original task, i.e. OSV, without sacrificing the
generalizability. Our proposed OSV system includes two steps: learning
representation and verification of the input signature. For the first step, the
signature images are fed into the trained Residual CNNs. The output
representations are then used to train SVMs for the verification. We test our
OSV system on three different signature datasets, including MCYT (a Spanish
signature dataset), UTSig (a Persian one) and GPDS-Synthetic (an artificial
dataset). On UT-SIG, we achieved 9.80% Equal Error Rate (EER) which showed
substantial improvement over the best EER in the literature, 17.45%. Our
proposed method surpassed state-of-the-arts by 6% on GPDS-Synthetic, achieving
6.81%. On MCYT, EER of 3.98% was obtained which is comparable to the best
previously reported results
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
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
Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data
Context. The direct detection of exoplanets with high-contrast imaging
requires advanced data processing methods to disentangle potential planetary
signals from bright quasi-static speckles. Among them, angular differential
imaging (ADI) permits potential planetary signals with a known rotation rate to
be separated from instrumental speckles that are either statics or slowly
variable. The method presented in this paper, called ANDROMEDA for ANgular
Differential OptiMal Exoplanet Detection Algorithm is based on a maximum
likelihood approach to ADI and is used to estimate the position and the flux of
any point source present in the field of view. Aims. In order to optimize and
experimentally validate this previously proposed method, we applied ANDROMEDA
to real VLT/NaCo data. In addition to its pure detection capability, we
investigated the possibility of defining simple and efficient criteria for
automatic point source extraction able to support the processing of large
surveys. Methods. To assess the performance of the method, we applied ANDROMEDA
on VLT/NaCo data of TYC-8979-1683-1 which is surrounded by numerous bright
stars and on which we added synthetic planets of known position and flux in the
field. In order to accommodate the real data properties, it was necessary to
develop additional pre-processing and post-processing steps to the initially
proposed algorithm. We then investigated its skill in the challenging case of a
well-known target, Pictoris, whose companion is close to the detection
limit and we compared our results to those obtained by another method based on
principal component analysis (PCA). Results. Application on VLT/NaCo data
demonstrates the ability of ANDROMEDA to automatically detect and characterize
point sources present in the image field. We end up with a robust method
bringing consistent results with a sensitivity similar to the recently
published algorithms, with only two parameters to be fine tuned. Moreover, the
companion flux estimates are not biased by the algorithm parameters and do not
require a posteriori corrections. Conclusions. ANDROMEDA is an attractive
alternative to current standard image processing methods that can be readily
applied to on-sky data
Planet transit and stellar granulation detection with interferometry
Aims. We used realistic three-dimensional (3D) radiative hydrodynamical (RHD)
simulations from the Stagger-grid and synthetic images computed with the
radiative transfer code Optim3D to provide interferometric observables to
extract the signature of stellar granulation and transiting planets. Methods.
We computed intensity maps from RHD simulations for twelve interferometric
instruments covering wavelengths ranging from optical to infrared. The stellar
surface asymmetries in the brightness distribution mostly affect closure
phases. We compared the closure phases of the system star with a transiting
planet and the star alone and considered the impact of magnetic spots
constructing a hypothetical starspots image. Results. All the simulations show
departure from the axisymmetric case at all wavelengths. We presented two
possible targets (Beta Com and Procyon) and found that departures up to 16 deg
can be detected on the 3rd lobe and higher. In particular, MIRC is the most
appropriate instrument because it combines good UV coverage and long baselines.
Moreover, we explored the impact of convection on interferometric planet
signature for three prototypes of planets. It is possible to disentangle the
signature of the planet at particular wavelengths (either in the infrared or in
the optical) by comparing the closure phases of the star at difference phases
of the planetary transit. Conclusions. The detection and characterisation of
planets must be based on a comprehensive knowledge of the host star; this
includes the detailed study of the stellar surface convection with
interferometric techniques. In this context, RHD simulations are crucial to
reach this aim. We emphasize that interferometric observations should be pushed
at high spatial frequencies by accumulating observations on closure phases at
short and long baselines.Comment: accepted in Astronomy and Astrophysics, 13 pages. Some figures have
reduced resolution to decrease the size of the output file. Please contact
[email protected] to have the high resolution version of the pape
On-line signature recognition through the combination of real dynamic data and synthetically generated static data
This is the author’s version of a work that was accepted for publication in Pattern Recognition . 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 , 48, 9 (2005) DOI: 10.1016/j.patcog.2015.03.019On-line signature verification still remains a challenging task within biometrics. Due to their behavioral nature (opposed
to anatomic biometric traits), signatures present a notable variability even between successive realizations. This
leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons
for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition
accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost
that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the
two modalities, we propose a method for the generation of enhanced synthetic static samples from on-line data. Such
synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination
of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new
on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of
the exact same signatures. Different findings and conclusions are drawn regarding the discriminative power of on-line
and off-line signatures and of their potential combination both in the random and skilled impostors scenarios.M. D.-C. is supported by a PhD fellowship from the
ULPGC and M.G.-B. is supported by a FPU fellowship
from the Spanish MECD. This work has been partially
supported by projects: MCINN TEC2012-38630-
C04-02, Bio-Shield (TEC2012-34881) from Spanish
MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK
and Cátedra UAM-Telefónic
Generation of enhanced synthetic off-line signatures based on real on-line data
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. M. Díaz-Cabrera, M. Gómez-Barrero, A. Morales, M. A. Ferrer, J. Galbally, "Generation of Enhanced Synthetic Off-Line Signatures Based on Real On-Line Data" in 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), Heraklion (Greece), 2014, 482 - 487One of the main challenges of off-line signature verification is the absence of large databases. A possible alternative to overcome this problem is the generation of fully synthetic signature databases, not subject to legal or privacy concerns. In this paper we propose several approaches to the synthesis of off-line enhanced signatures from real dynamic information. These synthetic samples show a performance very similar to the one offered by real signatures, even increasing their discriminative power under the skilled forgeries scenario, one of the biggest challenges of handwriting recognition. Furthermore, the feasibility of synthetically increasing the enrolment sets is analysed, showing promising results.This work has been partially supported by projects: MICINN TEC2012-38630-C04-02, Contexts (S2009/TIC-1485) from CAM, Bio-Shield (TEC2012-34881) from Spanish MINECO, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica
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