Hybrid User-Independent and User-Dependent Offline Signature Verification with a Two-Channel CNN

Signature verification task needs relevant signature representations to achieve low error rates. Many signature representations have been proposed so far. In this work we propose a hybrid user-independent/dependent offline signature verification technique with a two-channel convolutional neural network (CNN) both for verification and feature extraction. Signature pairs are input to the CNN as two channels of one image, where the first channel always represents a reference signature and the second channel represents a query signature. We decrease the image size through the network by keeping the convolution stride parameter large enough. Global average pooling is applied to decrease the dimensionality to 200 at the end of locally connected layers. We utilize the CNN as a feature extractor and report 4.13% equal error rate (EER) considering 12 reference signatures with the proposed 200-dimensional representation, compared to 3.66% of a recently proposed technique with 2048-dimensional representation using the same experimental protocol. When the two methods are combined at score level, more than 50% improvement (1.76% EER) is achieved demonstrating the complementarity of them. Sensitivity of the model to gray-level and binary images is investigated in detail. One model is trained using gray-level images and the other is trained using binary images. It is shown that the availability of gray-level information in train and test data decreases the EER e.g. from 11.86% to 4.13%. information about the claimed identity and they can usually be detected with very small error rates. Our focus in this work is offline signature verification where the query is either genuine signature or skilled forgery. Format of the signature image can be binary, gray-level or color. To the best of our knowledge, no public color signature database is available. However the distinction between binary and gray-level image is critical, as a gray-level image carries more information than the binary one such as the pressure of the stroke. Performance of signature verification systems is generally measured by equal error rate (EER) which is the error rate when the false accept (FA) and false reject (FR) rates are equal. If the EER is not reported, distinguishing error rate (DER) can be calculated which is simply the average of FA and FR.