Capturing Micro Deformations from Pooling Layers for Offline Signature Verification In this paper, we propose a novel Convolutional Neural Network (CNN) based method that extracts the location information (displacement features) of the maximums in the maxpooling operation and fuses it with the pooling features to capture the micro deformations between the genuine signatures and skilled forgeries as a feature extraction procedure. After the feature extraction procedure, we apply support vector machines (SVMs) as writerdependent classifiers for each user to build the signature verification system. The extensive experimental results on GPDS150, GPDS300, GPDS1000, GPDS2000, and GPDS5000 datasets demonstrate that the proposed method can discriminate the genuine signatures and their corresponding skilled forgeries well and achieve stateoftheart results on these datasets.

Speedingup the Handwritten Signature Segmentation Process through an Optimized Fully Convolutional Neural Network The handwritten signature is the most used method of identity authentication. Due to their nature, signatures can be used as an agreement in many types of documentation with legal repercussions. The validation of the firmed signature is used to prevent frauds, fake documents, and identity checking. However, working with automated signature verification is a challenging task because it can appear in any part of documents with complex backgrounds, with logos, handwritten texts, and many different patterns. Besides, the application needs to consider a realtime response. In this paper, we propose an optimized architecture of a fully convolutional neural network based on the UNet architecture for handwritten signature segmentation. Furthermore, we used data augmentation in order to increase the diversity of the available dataset and prevent the overfitting problem when training the proposed model. We conducted experiments with DSSigDataset, and we used four different data augmentation techniques to increase the dataset size. The experimental results show that our proposed approach speedup the handwritten signature segmentation task, at the same time, achieving higher accuracy and lower variance than previous works.