Do You Need More Data? The DeepSignDB OnLine Handwritten Signature Biometric Database Data have become one of the most valuable things in this new era where deep learning technology seems to overcome traditional approaches. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel approaches compared with the state of the art as different experimental protocols and conditions are usually considered for different signature databases. To tackle all these mentioned problems, the main contribution of this study is twofold: i) we present and describe the new DeepSignDB online handwritten signature biometric public database, and ii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art. The DeepSignDB database is obtained through the combination of some of the most popular online signature databases, and a novel dataset not presented yet. It comprises more than 70K signatures acquired using both stylus and finger inputs from a total of 1526 users. Two acquisition scenarios are considered, office and mobile, with a total of 8 different devices. Additionally, different types of impostors and number of acquisition sessions are considered along the database. The DeepSignDB and benchmark results are available in GitHub.

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.