  The use of features extracted using a deep convolutional neural network (CNN)
combined with a writer-dependent (WD) SVM classifier resulted in significant
improvement in performance of handwritten signature verification (HSV) when
compared to the previous state-of-the-art methods. In this work it is
investigated whether the use of these CNN features provide good results in a
writer-independent (WI) HSV context, based on the dichotomy transformation
combined with the use of an SVM writer-independent classifier. The experiments
performed in the Brazilian and GPDS datasets show that (i) the proposed
approach outperformed other WI-HSV methods from the literature, (ii) in the
global threshold scenario, the proposed approach was able to outperform the
writer-dependent method with CNN features in the Brazilian dataset, (iii) in an
user threshold scenario, the results are similar to those obtained by the
writer-dependent method with CNN features.
