Neighbour-based bag-of-samplings for person identification through handwritten dynamics and convolutional neural networks

Abstract

Biometric recognition provides straightforward methods to deal with the problem of identifying people under certain circumstances. Additionally, a well-calibrated biometric system enhances security policies and prevents malicious attempts, such as fraud or identity theft. Deep learning has arisen to foster the problem by extracting high-level features that compose the so-called ‘user fingerprint’, that is, digital identification of a particular individual. Nevertheless, personal identification is not a trivial task, as many traits might define an individual, varying according to the task's domain. An exciting way to overcome such a problem is to employ handwritten dynamics, which are hand- and motor-based signals from an individual's writing style and obtained through a biometric smartpen. In this work, we propose using such signals to identify an individual through convolutional neural networks. Essentially, the proposed work uses a neighbour-based bag-of-samplings procedure to sample the signals to a fixed size and feeds them into a neural network responsible for extracting their features and further classifying them. The experiments were conducted over two handwritten dynamic datasets, NewHandPD and SignRec, and established new fruitful state-of-the-art concerning these particular datasets and the corresponding context.