Discriminative training of spiking neural networks organised in columns for stream-based biometric authentication

Abstract

Stream-based biometric authentication using a novel approach based on spiking neural networks (SNNs) is addressed. SNNs have proven advantages regarding energy consumption and they are a perfect match with some proposed neuromorphic hardware chips, which can lead to a broader adoption of user device applications of artificial intelligence technologies. One of the challenges when using SNNs is the discriminative training of the network since it is not straightforward to apply the well-known error backpropagation (EBP), massively used in traditional artificial neural networks (ANNs). A network structure based on neuron columns is proposed, resembling cortical columns in the human cortex, and a new derivation of error backpropagation for the spiking neural networks that integrate the lateral inhibition in these structures. The potential of the proposed approach is tested in the task of inertial gait authentication, where gait is quantified as signals from Inertial Measurement Units (IMU), and the authors' approach to state-of-the-art ANNs is compared. In the experiments, SNNs provide competitive results, obtaining a difference of around 1% in half total error rate when compared to state-of-the-art ANNs in the context of IMU-based gait authentication.