425 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
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
Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification
This paper introduces a novel approach to leverage the knowledge of existing
expert models for training new Convolutional Neural Networks, on domains where
task-specific data are limited or unavailable. The presented scheme is applied
in offline handwritten signature verification (OffSV) which, akin to other
biometric applications, suffers from inherent data limitations due to
regulatory restrictions. The proposed Student-Teacher (S-T) configuration
utilizes feature-based knowledge distillation (FKD), combining graph-based
similarity for local activations with global similarity measures to supervise
student's training, using only handwritten text data. Remarkably, the models
trained using this technique exhibit comparable, if not superior, performance
to the teacher model across three popular signature datasets. More importantly,
these results are attained without employing any signatures during the feature
extraction training process. This study demonstrates the efficacy of leveraging
existing expert models to overcome data scarcity challenges in OffSV and
potentially other related domains
Auto Signature Verification Using Line Projection Features Combined With Different Classifiers and Selection Methods
: Signature verification plays a role in the commercial, legal and financial fields. The signature continues to be one of the most preferred types of authentication for many documents such as checks, credit card transaction receipts, and other legal documents. In this study, we propose a system for validating handwritten bank check signatures to determine whether the signature is original or forged. The proposed system includes several steps including improving the signature image quality, noise reduction, feature extraction, and analysis. The extracted features depend on the signature line and projection features. To verify signatures, different classification methods are used. The system is then trained with a set of signatures to demonstrate the validity of the proposed signature verification system. The experimental results show that the best accuracy of 100% was obtained by combining several classification methods
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication
Handwriting authentication is a valuable tool used in various fields, such as
fraud prevention and cultural heritage protection. However, it remains a
challenging task due to the complex features, severe damage, and lack of
supervision. In this paper, we propose a novel Contrastive Self-Supervised
Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address
these issues. It can dynamically learn complex yet important features and
accurately predict writer identities. Specifically, to remove the negative
effects of imperfections and redundancy, we design an information-theoretic
filter for pre-processing and propose a novel adaptive matching scheme to
represent images as patches of local regions dominated by more important
features. Through online optimization at inference time, the most informative
patch embeddings are identified as the "most important" elements. Furthermore,
we employ contrastive self-supervised training with a momentum-based paradigm
to learn more general statistical structures of handwritten data without
supervision. We conduct extensive experiments on five benchmark datasets and
our manually annotated dataset EN-HA, which demonstrate the superiority of our
CSSL-RHA compared to baselines. Additionally, we show that our proposed model
can still effectively achieve authentication even under abnormal circumstances,
such as data falsification and corruption.Comment: 10 pages, 4 figures, 3 tables, submitted to ACM MM 202
Human and Artificial Intelligence
Although tremendous advances have been made in recent years, many real-world problems still cannot be solved by machines alone. Hence, the integration between Human Intelligence and Artificial Intelligence is needed. However, several challenges make this integration complex. The aim of this Special Issue was to provide a large and varied collection of high-level contributions presenting novel approaches and solutions to address the above issues.
This Special Issue contains 14 papers (13 research papers and 1 review paper) that deal with various topics related to human–machine interactions and cooperation. Most of these works concern different aspects of recommender systems, which are among the most widespread decision support systems. The domains covered range from healthcare to movies and from biometrics to cultural heritage. However, there are also contributions on vocal assistants and smart interactive technologies.
In summary, each paper included in this Special Issue represents a step towards a future with human–machine interactions and cooperation. We hope the readers enjoy reading these articles and may find inspiration for their research activities
Features selection for offline handwritten signature verification: State of the art
This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification
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