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Off-line Signature Verification Using Writer-Independent Approach

Off-line Signature Verification Using Writer-Independent Approach,10.1109/IJCNN.2007.4371358,Luiz S. Oliveira,Edson J. R. Justino,Robert Sabourin

Off-line Signature Verification Using Writer-Independent Approach   (Citations: 5)
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In this work we present a strategy for off-line signature verification. It takes into account a writer-independent model which reduces the pattern recognition problem to a 2- class problem, hence, makes it possible to build robust signature verification systems even when few signatures per writer are available. Receiver Operating Characteristic (ROC) curves are used to improve the performance of the proposed system . The contribution of this paper is two-fold. First of all, we analyze the impacts of choosing different fusion strategies to combine the partial decisions yielded by the SVM classifiers. Then ROC produced by different classifiers are combined using maximum likelihood analysis, producing an ROC combined classifier. Through comprehensive experiments on a database composed of 100 writers, we demonstrate that the ROC combined classifier based on the writer-independent approach can reduce considerably false rejection rate while keeping false acceptance rates at acceptable levels. I. INTRODUCTION In the last few decades many methods have been de- veloped in pattern recognition area, regarding the signature verification problem, which can be categorized into on- line and off-line (13). In general, on-line systems achieve better performance since they can count on dynamic features such as, time, pressure, and speed, which can be easily obtained from the on-line mediums (11). On the other hand, off-line systems are difficult to design as many desirable characteristics such as the order of strokes, velocity, and other dynamic information are not available in the off-line case. The verification process has to rely only on the features that can be extracted from the trace of the static signature image (5). To deal with the problem of off-line signature verification, researchers have investigated two different approaches. The first one, and commonly used, is the writer-specific model (also known as personal model) which is based on two different pattern classes, !1 and !2, where !1 represents the genuine signature set, for a specific writer and !2 represents the forgery signatures set. The forgeries usually are divided into three different subsets (random, simple, and simulated forgeries). The random forgery is usually a genuine signature sample belonging to a different writer, one who is not necessarily enroled in the signature verification system. The simple forgery is a signature sample with the same shape as the genuine writer's signature. The simulated forgery is a reasonable imitation of the genuine signature model (8). The main drawbacks of the writer-specific approach are the need of learning the model each time a new writer should
Conference: International Symposium on Neural Networks - ISNN , pp. 2539-2544, 2007
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    • ...Despite the fact that the HMM is a generative classifier [5], which generally requires a considerable amount of training data to achieve a high level of performance, the HMM-based off-line SV systems are oftendesignedfromlimitedandunbalanceddata.Thisoccurs because the dataset used to model a writer generally contains a reduced number of genuine signatures against several random forgeries [25]...
    • ...shown that the Receiver Operating Characteristic (ROC) curve—where the true positive rates (TPR) are plotted as function of the false positive rates (FPR)—provides a powerful tool for evaluating, combining, and comparing off-line SV systems [3,25]...

    Luana Batistaet al. Improving performance of HMM-based off-line signature verification sys...

    • ...This figure is comparable to the results of other researchers [5, 10, 14, 16, 17] and can be explained by the LT values of the MDF, which were effectively generalized by the local averaging process...
    • ...However, some researchers observed an opposite trend regarding the proportion of random forgery errors as opposed to targeted forgery errors on other databases with different approaches [14, 17]...

    Vu Nguyenet al. Global Features for the Off-Line Signature Verification Problem

    • ...Though not fully explored in literature, it has recently been shown that the Receiver Operating Characteristic (ROC) curve - where the true positive rates ( TPR ) are plotted on the ya xis , while the false positive rates ( FPR )a re plotted on the xa xis- provides a powerful tool for evaluating, combining and comparing off-line SV systems [2] [10]...
    • ...This occurs because the dataset used to model a writer generally contains a reduced number of genuine signatures against several random forgeries [10]...

    Luana Batistaet al. A Multi-Hypothesis Approach for Off-Line Signature Verification with H...

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