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 Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach

Handwritten signature verification poses a formidable challenge in biometrics and document authenticity. The objective is to ascertain the authenticity of a provided handwritten signature, distinguishing between genuine and forged ones. This issue has many applications in sectors such as finance, legal documentation, and security. Currently, the field of computer vision and machine learning has made significant progress in the domain of handwritten signature verification. The outcomes, however, may be enhanced depending on the acquired findings, the structure of the datasets, and the used models. Four stages make up our suggested strategy. First, we collected a large dataset of 12600 images from 420 distinct individuals, and each individual has 30 signatures of a certain kind (All authors signatures are genuine). In the subsequent stage, the best features from each image were extracted using a deep learning model named MobileNetV2. During the feature selection step, three selectors neighborhood component analysis (NCA), Chi2, and mutual info (MI) were used to pull out 200, 300, 400, and 500 features, giving a total of 12 feature vectors. Finally, 12 results have been obtained by applying machine learning techniques such as SVM with kernels (rbf, poly, and linear), KNN, DT, Linear Discriminant Analysis, and Naive Bayes. Without employing feature selection techniques, our suggested offline signature verification achieved a classification accuracy of 91.3%, whereas using the NCA feature selection approach with just 300 features it achieved a classification accuracy of 97.7%. High classification accuracy was achieved using the designed and suggested model, which also has the benefit of being a self-organized framework. Consequently, using the optimum minimally chosen features, the proposed method could identify the best model performance and result validation prediction vectors.Comment: 11 page

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

Offline signature verification using writer-dependent ensembles and static classifier selection with handcraft features

Orientador: Eduardo TodtDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 17/02/2022Inclui referências: p. 85-94Área de concentração: Ciência da ComputaçãoResumo: Reconhecimento e identificação de assinaturas em documentos e manuscritos são tarefas desafiadoras que ao longo do tempo vêm sendo estudadas, em especial na questão de discernir assinaturas genuínas de falsificações. Com o recente avanço das tecnologias, principalmente no campo da computação, pesquisas nesta área têm se tornado cada vez mais frequentes, possibilitando o uso de novos métodos de análise das assinaturas, aumentando a precisão e a confiança na verificação delas. Ainda há muito o que se explorar em pesquisas desta área dentro da computação. Verificações de assinaturas consistem, de forma geral, em obter características acerca de um a assinatura e utilizá-las para discerni-la das demais. Estudos propondo variados tipos de métodos foram realizados nos últimos anos a fim de aprimorar os resultados obtidos por sistemas de verificação e identificação de assinaturas. Diferentes formas de extrair características têm sido exploradas, com o o uso de redes neurais artificiais voltadas especificam ente para verificação de assinaturas, como a ResNet e a SigNet, representando o estado-da-arte nesta área de pesquisa. Apesar disso, métodos mais simples de extração de características ainda são muito utilizados, como o histograma de gradientes orientados (HOG), o Local Binary Patterns (LBP) e Local Phase Quantization (LPQ) por exemplo, apresentando, em muitos casos, resultados similares ao estado-da-arte. Não apenas isso, mas diferentes formas de combinar informações de extratores de características e resultados de classificadores têm sido propostos, como é o caso dos seletores de características, métodos de comitê de máquinas e algoritmos de análise da qualidade das características. D esta form a, o trabalho realizado consiste em explorar diferentes métodos de extração de características com binados em um conjunto de classificadores, de maneira que cada conjunto seja construído de forma dependente do autor e seja especificam ente adaptado para reconhecer as melhores características para cada autor, aprendendo quais com binações de classificadores com determinado grupo de características melhor se adaptam para reconhecer suas assinaturas. O desempenho e a funcionalidade do sistema foram comparados com os principais trabalhos da área desenvolvidos nos últimos anos, tendo sido realizados testes com as databases CEDAR, M CYT e UTSig. A pesar de não superar o estado-da-arte, o sistema apresentou bom desempenho, podendo ser com parado com alguns outros trabalhos importantes na área. Além disso, o sistema mostrou a eficiência dos classificadores Support Vector M achine(SVM ) e votadores para a realização da meta-classificação, bem como o potencial de alguns extratores de características para a área de verificação de assinaturas, com o foi o caso do Compound Local Binary Pattern(CLBP).Abstract: Signature recognition and identification in documents and manuscripts are challenging tasks that have been studied over time, especially in the matter of discerning genuine signatures from forgeries. With the recent advancement of technologies, especially in the field of computing, research in this area has become increasingly frequent, enabling the use of new methods of analysis of signatures, increasing accuracy and confidence in their verification. There is still much to be explored in research in this area within computing. Signature verification generally consists in obtaining features about a signature and using them to distinguish it from others. Studies proposing different types o f methods have been carried out in recent years in order to improve the results obtained by signature verification and identification systems. Different ways of extracting features have been explored, such as the use of artificial neural networks specifically aimed at verifying signatures, like ResNet and SigNet, representing the state-of-the-art in this research area. Despite this, simpler methods of feature extraction are still widely used, such as the Histogram of Oriented Gradients (HOG), the Local Binary Patterns (LBP) and the Local Phase Quantization (LPQ) for example, presenting, in many cases, similar results to the state-of-the-art. Not only that, but different ways of combining information from feature extractors and results from classifiers have been proposed, such as feature selectors, machine committee methods and feature quality analysis algorithms. In this way, the developed work consists in exploring different methods of features extractors combined in an ensemble, so that each ensemble is built in a writer-dependent way and is specifically adapted to recognize the best features for each author, learning which combinations of classifiers with a certain group of characteristics is better adapted to recognize their signatures. The performance and functionality of the system were compared w ith the m ain works in the area developed in recent years, w ith tests having been carried out with the CEDAR, M CYT and UTSig databases. Despite not overcoming the state-of-the-art, the system presented good performance, being able to be compared with some other important works in the area. In addition, the system showed the efficiency of Support Vector Machine(SVM ) classifiers and voters to perform the meta-classification, as well as the potential of some feature extractors for the signature verification area, such as the Compound Local Binary Pattern(CLBP)