Off-line Signature Verification: An Approach Based on Combining Distances and One-class Classifiers

Souza, M. R. P.; Cavalcanti, George D. C.; Ren, Tsang Ing

doi:10.1109/ictai.2010.10

Cited by 4 publications

(5 citation statements)

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“…There are different options for distance calculation such as dmin/max which is minimum/maximum distance between a signature and the patterns of the reference set, and dcentral which is the distance between a signature and the center of mass of the reference set [17]. One of the important parameter in verification system is the threshold value for accepting or rejecting a signature.…”

Section: Classificationmentioning

confidence: 99%

“…In global threshold, the system will choose one threshold value for all users. On the other hand, for local threshold, the system must choose one threshold per user so that, this approach could lead to a better result [17].…”

Section: Classificationmentioning

confidence: 99%

“…In contrast with multi-class classifiers, the aim for one-class classifiers is distinguishing one type of class (target) from other classes (outlier). Thus, For classifying a signature as genuine or forgery, one-class classifiers have been commonly used [17] to divide the set into two categories: target and outlier ( Figure 2). Jain and Gangrade [7] proposed a system by using angle, energy and chain code features to diffrentiate the signatures.…”

Section: Classificationmentioning

confidence: 99%

“…Souza, et al [17] proposed an off-line signature verification system, which uses a combination of five distance measurements, such as, furthest, nearest, template and central using four operations: product, mean, maximum, and minimum as a feature vector.…”

Section: Classificationmentioning

confidence: 99%

See 3 more Smart Citations

Online signature verification based on feature representation

Fayyaz

Saffar

Sabokrou

et al. 2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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Abstract-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, which has enormous impact on the accuracy of signature verification. This paper presents a method based on self-taught learning, in which a sparse autoencoder attempts to learn discriminative features of signatures from a large unlabeled signature dataset. Then, the features learned are employed to present users' signatures by creating a model for each user based on user genuine signatures. Finally, users' signatures are classified using a one-class classifier. The proposed method is independent on signature datasets thanks to self-taught learning. The features have been learned from 17,500 signatures (ATVS dataset) and verification process of the proposed system is evaluated on SVC2004 and SUSIG signature datasets, which contain genuine and skilled forgery signatures. The experimental results indicate significant error reduction and accuracy enhancement in comparison with state of the art counterparts.

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Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Online signature verification based on feature representation

Fayyaz

Saffar

Sabokrou

et al. 2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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show abstract

“…Tais aplicações, que comumente apresentam conjuntos de dados altamente complexos, têm sido reportadas em um grande número de domínios, tais como diagnóstico médico (Sun et al, 2007;Braga et al, 2008;Natowicz et al, 2008;Silva et al, 2009;Moturu et al, 2010), suporte à decisão em unidades de tratamento intensivo (Morik et al, 1999), detecção de fraudes/falhas Carvalho et al, 2008;Gao et al, 2009), categorização de texto (Li and Shawe-Taylor, 2003;Manevitz and Yousef, 2007), reconhecimento de assinaturas (Souza et al, 2010), monitoramento de quebras de eixos automotivos (Hong et al, 2007), identificação de alertas de colisão entre aeronaves (Everson and Fieldsend, 2006b), entre outros.…”

Section: Introductionunclassified

Aprendizado supervisionado com conjuntos de dados desbalanceados

Castro

Braga

2011

Sba Controle & Automação

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Supervised Learning with Imbalanced Data Sets: An OverviewTraditional learning algorithms induced by complex and highly imbalanced training sets may have difficulty in distinguishing between examples of the groups. The tendency is to create classification models that are biased toward the overrepresented (majority) class, resulting in a low rate of recognition for the minority group. This paper provides a survey of this problem which has attracted the interest of many researchers in recent years. In the scope of two-class classification tasks, concepts related to the nature of the imbalanced class problem and evaluation metrics are presented, including the foundations of the ROC (Receiver Operating Characteristic) analysis; plus a state of the art of the proposed solutions. At the end of the paper a brief discussion on how the subject can be extended to multiclass learning is provided.KEYWORDS: imbalanced data sets, supervised learning, evaluation metrics, ROC analysis, resampling methods, costsensitive approach. RESUMOAlgoritmos de aprendizado tradicionais induzidos por conjuntos de treinamento complexos e altamente desbalanceados têm apresentado dificuldade em diferenciar entre os grupos. A tendência é produzir modelos (ou regras) de classificação que favorecem a classe com maior probabilidade de ocorrên-cia (majoritária), resultando em uma baixa taxa de reconhecimento para o grupo minoritário. O objetivo desse artigo é fornecer uma investigação sobre esse problema, que tem atraído o interesse de muitos pesquisadores nos últimos anos. No escopo de tarefas de classificação binária, são apresentados conceitos associados à natureza do problema de classes desbalanceadas e métricas de avaliação, incluindo os fundamentos da análise ROC (Receiver Operating Characteristic); além do estado da arte das soluções propostas na literatura. Uma breve discussão a respeito de como os tópicos abordados no artigo podem ser estendidos para o aprendizado multiclasse é também fornecida. PALAVRAS-CHAVE:classes desbalanceadas, aprendizado supervisionado, métricas de avaliação, análise ROC, méto-dos de reamostragem, abordagem sensível ao custo.

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BeeOWA: A novel approach based on ABC algorithm and induced OWA operators for constructing one-class classifier ensembles

Parhizkar

Abadi

2015

Neurocomputing

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Off-line Signature Verification: An Approach Based on Combining Distances and One-class Classifiers

Cited by 4 publications

References 5 publications

Online signature verification based on feature representation

Online signature verification based on feature representation

Aprendizado supervisionado com conjuntos de dados desbalanceados

BeeOWA: A novel approach based on ABC algorithm and induced OWA operators for constructing one-class classifier ensembles

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