A StrokeBased RNN for WriterIndependent Online Signature Verification In the field of online handwritten signature verification, it is challenging to verify handwritten signature in a writerindependent scenario. In recent years, many researchers have been applying deep neural network methods to the signature verification task. However, these methods have not outperformed traditional methods, especially when the training samples are limited. In this paper, we propose a novel strokebased bidirectional RNN architecture. The main idea is to split the signature into multiple patches using strokes. Concatenation of query and reference signature pairs are used as input. The proposed method uses two LSTM RNN networks to extract different features. The first one extracts the features of the strokes and the latter extracts the global features of the whole signatures. The results on the BiosecureID dataset demonstrate that our proposed method can reduce the EER by 33.05%, from 5.6% to 3.75% with fewer features and less training samples. Besides, we find that the proposed stroke based RNN network is 5x faster in training and testing time than Non strokebased RNN network.

DeepSignCX: Signature Complexity Detection using Recurrent Neural Networks This paper proposes a novel approach for online signature complexity detection based on Recurrent Neural Networks (RNNs). Complexity of handwritten signatures can vary from very simple ones (just a simple flourish) to very complex signatures (including the handwritten full name and complex flourish). Three different complexity levels are proposed: low, medium, and high. Time functions are extracted from the online signatures and a system based on RNNs (BLSTM in particular) is trained to classify the three levels of complexity over a ground truth manually labelled database (BiosecurID with 400 subjects). This initial model is used to automatically label a very large database (DeepSignDB) containing over 1500 subjects, which is then used to train the proposed RNN for signature complexity detection. Promising results ca. 85% of accuracy are achieved. This complexity detector could be used as a first stage in a signature verification system in order to train a specific biometric system per signature complexity level and improve the overall system performance.