Writer independent offline signature verification based on asymmetric pixel relations and unrelated training-testing datasets

Offline writer independent (WI) signature verification is conceivably a challenging task in the domain of handwritten biometrics. This work addresses it by introducing a feature extraction scheme that relies on the detection of first order transitions between asymmetrical lattice arrangements of simple pixel structures. The experiments are conducted with a decision stumps committee, accompanied with boosting feature selection, by employing only unrelated or blind training and testing datasets, all derived from four widely used signature databases. In addition, a fifth signature dataset which contains disguised signatures, originating from a signature verification contest, is also used for testing purposes. The impact of the preprocessing stage per dataset is exploited by allowing various pruning levels of the raw binary signature. In addition to standard training protocols, we introduce the use of the applicability domain (AD) in WI signature verification and examine its effectiveness in providing reliable classifier predictions by quantifying subregions of the input distance space that have been sufficiently covered by training examples. The derived experimental results expressed by means of equal error rate, along with best average error rate, are considered to be very competitive to those provided from state of the art WI methods. (C) 2019 Elsevier Ltd. All rights reserved.