Diego Bertolini

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In this work we address two important issues of off-line signature verification. The first one regards feature extraction. We introduce a new graphometric feature set that considers the curvature of the most important segments, perceptually speaking, of the signature. The idea is to simulate the shape of the signature by using Bezier curves and then extract(More)
In this work we present a method for selecting instances for a writer identification system underpinned on the dissimilarity representation and a holistic representation based on texture. The proposed method is based on a genetic algorithm that surpasses the limitations imposed by large training sets by selecting writers instead of instances. To show the(More)
—In this study we assess the performance of textural descriptors for writer identification on different writing styles and also on forgeries. To do that, we have performed a series of experiments using the Firemaker database, which provides for the same writer texts written on three different writing styles and also copied forged text. Our experimental(More)
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