37,210 research outputs found
Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
Biometric authentication by means of handwritten signatures is a challenging
pattern recognition task, which aims to infer a writer model from only a
handful of genuine signatures. In order to make it more difficult for a forger
to attack the verification system, a promising strategy is to combine different
writer models. In this work, we propose to complement a recent structural
approach to offline signature verification based on graph edit distance with a
statistical approach based on metric learning with deep neural networks. On the
MCYT and GPDS benchmark datasets, we demonstrate that combining the structural
and statistical models leads to significant improvements in performance,
profiting from their complementary properties
A graph-based aspect interference detection approach for UML-based aspect-oriented models
Aspect Oriented Modeling (AOM) techniques facilitate separate modeling of concerns and allow for a more flexible composition of these than traditional modeling technique. While this improves the understandability of each submodel, in order to reason about the behavior of the composed system and to detect conflicts among submodels, automated tool support is required. Current techniques for conflict detection among aspects generally have at least one of the following weaknesses. They require to manually model the abstract semantics for each system; or they derive the system semantics from code assuming one specific aspect-oriented language. Defining an extra semantics model for verification bears the risk of inconsistencies between the actual and the verified design; verifying only at implementation level hinders fixng errors in earlier phases. We propose a technique for fully automatic detection of conflicts between aspects at the model level; more specifically, our approach works on UML models with an extension for modeling pointcuts and advice. As back-end we use a graph-based model checker, for which we have defined an operational semantics of UML diagrams, pointcuts and advice. In order to simulate the system, we automatically derive a graph model from the diagrams. The result is another graph, which represents all possible program executions, and which can be verified against a declarative specification of invariants.\ud
To demonstrate our approach, we discuss a UML-based AOM model of the "Crisis Management System" and a possible design and evolution scenario. The complexity of the system makes conĀ°icts among composed aspects hard to detect: already in the case of two simulated aspects, the state space contains 623 diĀ®erent states and 9 different execution paths. Nevertheless, in case the right pruning methods are used, the state-space only grows linearly with the number of aspects; therefore, the automatic analysis scales
Adaptive feature thresholding for off-line signature verification
This paper introduces Adaptive Feature Thresholding (AFT) which is a novel method of person-dependent off-line signature verification. AFT enhances how a simple image feature of a signature is converted to a binary feature vector by significantly improving its representation in relation to the training signatures. The similarity between signatures is then easily computed from their corresponding binary feature vectors. AFT was tested on the CEDAR and GPDS benchmark datasets, with classification using either a manual or an automatic variant. On the CEDAR dataset we achieved a classification accuracy of 92% for manual and 90% for automatic, while on the GPDS dataset we achieved over 87% and 85% respectively. For both datasets AFT is less complex and requires fewer images features than the existing state of the art methods, while achieving competitive 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
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