EEG-Based Biometric Verification Using Siamese CNNs

Cognitive biometric characteristics have recently attracted the attention of the scientific community thanks to some of their interesting properties, such as their intrinsic liveness detection capability and their robustness against spoofing attacks. Among the traits belonging to this category, brain signals have been considered in several studies, commonly focusing on the analysis of electroencephalography (EEG) recordings. Unfortunately, a significant intra-class variability affects EEG data acquired at different times, making it therefore hard for current state-of-the-art methods to achieve high recognition rates. To cope with this issue, deep learning techniques have been recently employed to search for EEG discriminative information, yet only identification scenarios have been so far considered in literature. In this paper a verification context is instead taken into account, and proper networks are proposed to extract features allowing to differentiate subjects which are not available during network training, by resorting to siamese designs. The performed experimental tests, conducted over a longitudinal database comprising EEG acquisitions taken during five sessions spanning a period of one year and a half, show the effectiveness of the proposed approach in achieving high-level accuracy for brain-based biometric verification purposes.