Author
Edson J. R. Justino
Bio: Edson J. R. Justino is an academic researcher from Pontifícia Universidade Católica do Paraná. The author has contributed to research in topics: Support vector machine & Hidden Markov model. The author has an hindex of 16, co-authored 49 publications receiving 1475 citations.
Papers published on a yearly basis
Papers
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10 Sep 2001TL;DR: The experiments have shown that the error rates of the simple and random forgery signatures are very closed, and this reflects the real applications in which the simple forgeries represent the principal fraudulent case.
Abstract: The problem of signature verification is in theory a pattern recognition task used to discriminate two classes, original and forgery signatures. Even after many efforts in order to develop new verification techniques for static signature verification, the influence of the forgery types has not been extensively studied. This paper reports the contribution to signature verification considering different forgery types in an HMM framework. The experiments have shown that the error rates of the simple and random forgery signatures are very closed. This reflects the real applications in which the simple forgeries represent the principal fraudulent case. In addition, the experiments show promising results in skilled forgery verification by using simple static and pseudodynamic features.
201 citations
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TL;DR: A comparison of the two classifiers in off-line signature verification using random, simple and simulated forgeries to observe the capability of the classifiers to absorb intrapersonal variability and highlight interpersonal similarity.
199 citations
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TL;DR: A new graphometric feature set that considers the curvature of the most important segments, perceptually speaking, of the signature by using Bezier curves and then extracting features from these curves to improve the reliability of the classification.
178 citations
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TL;DR: Through a series of comprehensive experiments, this work shows that both LBP- and LPQ-based classifiers are able to surpass previous results reported in the literature for the verification problem by about 5 percentage points, and the proposed approach using LPQ features is able to achieve accuracies of 96.7% and 99.2% on the BFL and IAM and databases respectively.
Abstract: Highlights? A segmentation free process for writer identification/verification. ? Evaluation of two texture descriptors (LBP and LPQ) for writer identification/verification. ? Evaluation of the dissimilarity-based approach for writer identification. ? Discussion about the number and size of the references for the dissimilarity-based approach. In this work, we discuss the use of texture descriptors to perform writer verification and identification. We use a classification scheme based on dissimilarity representation, which has been successfully applied to verification problems. Besides assessing two texture descriptors (local binary patterns and local phase quantization), we also address important issues related to the dissimilarity representation, such as the impact of the number of references used for verification and identification, how the framework performs on the problem of writer identification, and how the dissimilarity-based approach compares to other feature-based strategies. In order to meet these objectives, we carry out experiments on two different datasets, the Brazilian forensic letters database and the IAM database. Through a series of comprehensive experiments, we show that both LBP- and LPQ-based classifiers are able to surpass previous results reported in the literature for the verification problem by about 5 percentage points. For the identification problem, the proposed approach using LPQ features is able to achieve accuracies of 96.7% and 99.2% on the BFL and IAM and databases respectively.
157 citations
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TL;DR: The proposed method first applies a polygonal approximation in order to reduce the complexity of the boundaries and then extracts relevant features of the polygon to carry out the local reconstruction.
100 citations
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01 Sep 2008TL;DR: This paper presents the state of the art in automatic signature verification and addresses the most valuable results obtained so far and highlights the most profitable directions of research to date.
Abstract: In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.
688 citations
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TL;DR: Three scenarios are considered here for which solutions to the basic attribution problem are inadequate; it is shown how machine learning methods can be adapted to handle the special challenges of that variant.
Abstract: Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample. In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant. © 2009 Wiley Periodicals, Inc.
523 citations
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TL;DR: It is shown experimentally that the machine expert based on local information outperforms the system based on global analysis when enough training data is available and it is found that global analysis is more appropriate in the case of small training set size.
Abstract: An on-line signature verification system exploiting both local and global information through decision-level fusion is presented. Global information is extracted with a feature-based representation and recognized by using Parzen Windows Classifiers. Local information is extracted as time functions of various dynamic properties and recognized by using Hidden Markov Models. Experimental results are given on the large MCYT signature database (330 signers, 16500 signatures) for random and skilled forgeries. Feature selection experiments based on feature ranking are carried out. It is shown experimentally that the machine expert based on local information outperforms the system based on global analysis when enough training data is available. Conversely, it is found that global analysis is more appropriate in the case of small training set size. The two proposed systems are also shown to give complementary recognition information which is successfully exploited using decision-level score fusion.
355 citations
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TL;DR: An extensive review of biometric technology is presented here, focusing on mono-modal biometric systems along with their architecture and information fusion levels.
351 citations