Longitudinal Evaluation of EEG-Based Biometric Recognition

• Published in: IEEE transactions on information forensics and security Vol. 13; no. 5; pp. 1123 - 1138
• Main Authors: Maiorana, Emanuele, Campisi, Patrizio
• Format: Journal Article
• Language: English
• Published: IEEE 01.05.2018
• Subjects: Aging; aging effects; Biometrics; Electroencephalography; Iris recognition; longitudinal data analysis; permanence; Protocols
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2017.2778010

Brain signals have recently attracted the attention of the scientific community as potential biometric identifiers. In more detail, there is a growing interest in evaluating the feasibility of using electroencephalography (EEG) recordings to perform automatic people recognition. In this scenario, the study of the longitudinal behavior of EEG signals, i.e., their permanence across time, is of paramount importance. This paper is the first extensive attempt, in terms of employed elicitation protocols, number of involved subjects, number of acquisition sessions, and covered time span, to evaluate the influence of aging effects on the discriminative capabilities of EEG signals over long-term periods. Specifically, we here report and discuss the results obtained from experimental tests conducted on a database comprising 45 subjects, whose EEG signals have been collected during five to six distinct sessions spanning a total period of three years, using four different elicitation protocols. The longitudinal behavior of EEG discriminative traits is evaluated by means of a statistical-and performance-related analysis, using different EEG features and hidden Markov models as classifiers. A characterization of each considered EEG channel in terms of uniqueness and permanence properties is also performed, with the purpose of ranking their relevance for biometric purposes, thus giving hints to contain their number in practical applications. Moreover, we design some possible countermeasures to mitigate aging effects on recognition performance and evaluate their effectiveness, thus paving the road for the future deployment of real-life cognitive recognition systems relying on brain-based biometric traits.