483 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
Graph-Based Offline Signature Verification
Graphs provide a powerful representation formalism that offers great promise
to benefit tasks like handwritten signature verification. While most
state-of-the-art approaches to signature verification rely on fixed-size
representations, graphs are flexible in size and allow modeling local features
as well as the global structure of the handwriting. In this article, we present
two recent graph-based approaches to offline signature verification: keypoint
graphs with approximated graph edit distance and inkball models. We provide a
comprehensive description of the methods, propose improvements both in terms of
computational time and accuracy, and report experimental results for four
benchmark datasets. The proposed methods achieve top results for several
benchmarks, highlighting the potential of graph-based signature verification
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
Learning features for offline handwritten signature verification
Handwritten signatures are the most socially and legally accepted means for identifying a person. Over the last few decades, several researchers have approached the problem of automating their recognition, using a variety of techniques from machine learning and pattern recognition. In particular, most of the research effort has been devoted to obtaining good feature representations for signatures, by designing new feature extractors, as well as experimenting with feature extractors developed for other purposes. To this end, researchers have used insights from graphology, computer vision, signal processing, among other areas. In spite of the advancements in the field, building classifiers that can separate between genuine signatures and skilled forgeries (forgeries made targeting a particular individual) is still an open research problem.
In this thesis, we propose to address this problem from another perspective, by learning the feature representations directly from signature images. The hypothesis is that, in the absence of a good model of the data generation process, it is better to learn the features from data. As a first contribution, we propose a method to learn Writer-Independent features using a surrogate objective, followed by training Writer-Dependent classifiers using the learned features. Furthermore, we define an extension that allows leveraging the knowledge of skilled forgeries (from a subset of users) in the feature learning process. We observed that such features generalize well to new users, obtaining state-of-the-art results on four widely used datasets in the literature.
As a second contribution, we investigate three issues of signature verification systems: (i) learning a fixed-sized vector representation for signatures of varied size; (ii) analyzing the impact of the resolution of the scanned signatures in system performance and (iii) how features generalize to new operating conditions with and without fine-tuning. We propose methods to handle signatures of varied size and our experiments show results comparable to state-of-theart while removing the requirement that all input images have the same size.
As a third contribution, we propose to formulate the problem of signature verification as a meta-learning problem. This formulation also learns directly from signatures images, and allows the direct optimization of the objective (separating genuine signatures and skilled forgeries), instead of relying on surrogate objectives for learning the features. Furthermore, we show that this method is naturally extended to formulate the adaptation (training) for new users as one-class classification.
As a fourth contribution, we analyze the limitations of these systems in an Adversarial Machine Learning setting, where an active adversary attempts to disrupt the system. We characterize new threats posed by Adversarial Examples on a taxonomy of threats to biometric systems, and conduct extensive experiments to evaluate the success of attacks under different scenarios of attacker’s goals and knowledge of the system under attack. We observed that both systems that rely on handcrafted features, as well as those using learned features, are susceptible to adversarial attacks in a wide range of scenarios, including partial-knowledge scenarios where the attacker does not have full access to the trained classifiers. While some defenses proposed in the literature increase the robustness of the systems, this research highlights the scenarios where such systems are still vulnerable
Automatic intrapersonal variability modeling for offline signature augmentation
Orientador: Luiz Eduardo Soares de OliveiraCoorientadores: Robert Sabourin e Alceu de Souza Britto Jr..Tese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 19/07/2021Inclui referências: p. 93-102Área de concentração: Ciência da ComputaçãoResumo: Normalmente, em um cenario do mundo real, poucas assinaturas estao disponiveis para treinar um sistema de verificacao automatica de assinaturas (SVAA). Para resolver esse problema, diversas abordagens para a duplicacao de assinaturas estaticas foram propostas ao longo dos anos. Essas abordagens geram novas amostras de assinaturas sinteticas aplicando algumas transformacoes na imagem original da assinatura. Algumas delas geram amostras realistas, especialmente o duplicator. Este metodo utiliza um conjunto de parametros para modelar o comportamento do escritor (variabilidade do escritor) ao assinar. No entanto, esses parametros so empiricamente definidos. Este tipo de abordagem pode ser demorado e pode selecionar parametros que nao descrevem a real variabilidade do escritor. A principal hipotese desse trabalho e que a variabilidade do escritor observada no dominio da imagem tambem pode ser transferido para o dominio de caracteristicas. Portanto, este trabalho propoe um novo metodo para modelar automaticamente a variabilidade do escritor para a posterior duplicacao de assinaturas no dominio de imagem (duplicator) e dominio de caracteristicas (filtro Gaussiano e variacao do metodo de Knop). Este trabalho tambem propoe um novo metodo de duplicacao de assinaturas estaticas, que gera as amostras sinteticas diretamente no dominio de caracteristicas usando um filtro Gaussiano. Alem disso, uma nova abordagem para avaliar a qualidade de amostras sinteticas no dominio de caracteristicas e apresentada. As limitacoes e vantagens de ambas as abordagens de duplicacao de assinaturas tambem sao exploradas. Alem de usar a nova abordagem para avaliar a qualidade das amostras, o desempenho de um SVAA e avaliado usando as amostras e tres bases de assinaturas estaticas bem conhecidas: a GPDS-300, a MCYT-75 e a CEDAR. Para a mais utilizada, GPDS-300, quando o classificador SVM foi treinando com somente uma assinatura genuina por escritor, ele obteve um Equal Error Rate (EER) de 5,71%. Quando o classificador tambem utilizou as amostras sinteticas geradas no dominio de imagem, o EER caiu para 1,08%. Quando o classificador foi treinado com as amostras geradas pelo filtro Gaussiano, o EER caiu para 1,04%.Abstract: Normally, in a real-world scenario, there are few signatures available to train an automatic signature verification system (ASVS). To address this issue, several offline signature duplication approaches have been proposed along the years. These approaches generate a new synthetic signature sample applying some transformations in the original signature image. Some of them generate realistic samples, specially the duplicator. This method uses a set of parameters to model the writer's behavior (writer variability) during the signing act. However, these parameters are empirically defined. This kind of approach can be time consuming and can select parameters that do not describe the real writer variability. The main hypothesis of this work is that the writer variability observed in the image space can be transferred to the feature space as well. Therefore, this work proposes a new method to automatically model the writer variability for further signature duplication in the image (duplicator) and the feature space (Gaussian filter and a variation of Knop's method). This work also proposes a new offline signature duplication method, which directly generates the synthetic samples in the feature space using a Gaussian filter. Furthermore, a new approach to assess the quality of the synthetic samples in the feature space is introduced. The limitations and advantages of both signature augmentation approaches are also explored. Despite using the new approach to assess the quality of the samples, the performance of an ASVS was assessed using them and three well-known offline signature datasets: GPDS-300, MCYT-75, and CEDAR. For the most used one, GPDS-300, when the SVM classifier was trained with only one genuine signature per writer, it achieved an Equal Error Rate (EER) of 5.71%. When the classifier also was trained with the synthetic samples generated in the image space, the EER dropped to 1.08%. When the classifier was trained using the synthetic samples generated by the Gaussian filter, the EER dropped to 1.04%
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