Preliminary Study on the Behavioral Traits Obtained from Signatures and Writing Using Deep Learning Algorithms

Abstract

This work describes how a new multimodal data base is constructed to try to solve the link between signature/handwriting and personality. With the help of two devices, one of them responsible to report the mechanical writing process (using a tablet) and the other one to acquire brain activity (with an EEG headset) we will be able to carry out different sessions through a set of experiments. Because the data base is not completed yet, and it is well known that deep learning requires larges amount of data, the main results about signature and personality factors were not good enough. The different deep convolutional neural networks (DCNN) tested does not obtain a reasonable minimum threshold. However the same incomplete data base gives promising results when solving a completely different problem such as signature recognition (where a performance of 80% was reached) using the same DCNN architecture.