EEG-based biometrics: Effects of template ageing

Arnau-González, Pablo; Katsigiannis, Stamos; Arevalillo-Herráez, Miguel; Ramzan, Naeem

Abstract

This chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do this, EEG signals are characterised with common state-of-the-art features, i.e. Mel Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power Spectral Density-derived features. The samples were later classified using various classifiers, including Support Vector Machines and k-Nearest Neighbours with different parametrisations. Results show that performance tends to be worse for crosssession identification compared to single session identification. This finding suggests that temporal permanence of EEG signals is limited and thus more sophisticated methods are needed in order to characterise EEG signals for the task of subject identification

Citation

Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2020). EEG-based biometrics: Effects of template ageing. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

• Online Publication Date: Dec 15, 2020
• Publication Date: 2020
• Deposit Date: Dec 15, 2020
• Publicly Available Date: Jan 13, 2021
• Publisher: IET
• Book Title: AI for Emerging Verticals; Human-robot computing, sensing and networking.
• Public URL: https://durham-repository.worktribe.com/output/1649581
• Publisher URL: https://shop.theiet.org/ai-for-emerging-verticals