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The use of features extracted using a deep convolutional neural network (CNN)combined with a writer-dependent (WD) SVM classifier resulted in significantimprovement in performance of handwritten signature verification (HSV) whencompared to the previous state-of-the-art methods. In this work it isinvestigated whether the use of these CNN features provide good results in awriter-independent (WI) HSV context, based on the dichotomy transformationcombined with the use of an SVM writer-independent classifier. The experimentsperformed in the Brazilian and GPDS datasets show that (i) the proposedapproach outperformed other WI-HSV methods from the literature, (ii) in theglobal threshold scenario, the proposed approach was able to outperform thewriter-dependent method with CNN features in the Brazilian dataset, (iii) in anuser threshold scenario, the results are similar to those obtained by thewriter-dependent method with CNN features.

A writer-independent approach for offline signature verification using deep convolutional neural networks features