Use of spontaneous blinking for application in human authentication

Jalilifard, Amir; Chen, Dehua; Mutasim, Aunnoy K; Bashar, M. Raihanul; Tipu, Rayhan Sardar; Shawon, Ahsan-Ul Kabir; Sakib, Nazmus; Amin, M. Ashraful; Islam, Kafiul

doi:10.1016/j.jestch.2020.05.007

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…RSVP is the process of sequentially displaying images in the same spatial position at a high presentation rate with multiple images per second (such as 2-20 Hz) [8]. The RSVP protocol is often combined with eye-blinking activity for optimizing artifact capturing, since the rate of abrupt change in visual stimuli can be combined with the rate of blinking [32,73]. RSVP produces robust potential differences compared to traditional ERP protocols, making the acquired data cleaner and more artifact-free [73,76,77,80].…”

Section: B External Stimuli-based Protocolsmentioning

confidence: 99%

A Review of EEG-Based User Authentication: Trends and Future Research Directions

Fidas

Lyras²

2023

IEEE Access

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The employment of Electroencephalography (EEG) in the User Authentication (UA) scientific research has recently unlocked state-of-the-art experimentation, aiming at identifying and authenticating individuals given their brainiac activity within specific contexts of use. Indeed, utilizing EEG signals that are derived from brainiac activities can be used for tackling existing UA security threats such as shoulder surfing, thus providing a novel solution to contemporary security problems in traditional knowledge-based user authentication. In this survey, we aim to complete previous literature surveys by providing a systematic classification and presentation of existing research that is based on the following pillars: a) the user experimental setup, with an emphasis on the applied EEG-acquisition protocols (e.g., rest, external stimuli driven, mental and hybrid); b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad area of experiment protocols and various algorithms used for EEG signal classification. Moreover, most cited works include results from more than one experiments with different approaches and configurations. This leads to a discussion on best practices for EEG-based User Authentication and conclusions suggesting future research directions that consists, among others, of considering homomorphically encrypted biometric templates for information leakage prevention through federated learning approaches in decentralized architectures. We anticipate that the present literature review will provide a roadmap for future research by considering efficiently and effective EEG-based User Authentication methods while at the same time preserving privacy.

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Section: B External Stimuli-based Protocolsmentioning

confidence: 99%

A Review of EEG-Based User Authentication: Trends and Future Research Directions

Fidas

Lyras²

2023

IEEE Access

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“…Furthermore, it has achieved a high recognition rate of up to >90.0% in identification mode and a low false acceptance rate (FAR) among multiple persons [ 12 ]. Moreover, a multi-level EEG-based authentication approach with EOG signals has also been reported in the limited literature [ 13 , 14 ]. Jalilifard et al have developed a hybrid human authentication system using EEG and EOG signals generated by involuntary spontaneous blinking eye movements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The amplitude of the EOG signals occurs between 10 and 200 µV and has been observed to change at frequencies falling within the 0.5–15 Hz range. The eyeball acts as a dipole and charged organ [ 12 , 13 , 14 ]. The positive and negative poles are oriented anteriorly (cornea) and posteriorly (retina).…”

Section: Introductionmentioning

confidence: 99%

Fourier Synchrosqueezing Transform-ICA-EMD Framework Based EOG-Biometric Sustainable and Continuous Authentication via Voluntary Eye Blinking Activities

Gorur

2023

Biomimetics

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In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication (one-versus-others verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye blinkings movements. FSST is implemented to provide accurate and dense temporal-spatial properties of EOGs on the state-of-the-art time-frequency matrix. ICA is a powerful statistical tool to decompose multiple recording electrodes. Finally, EMD is deployed to isolate EOG signals from the EEGs collected from the scalp. As our best knowledge, this is the first research attempt to explore the success of the FSST-ICA-EMD framework on EOG-biometric authentication generated via voluntary eye blinking activities in the limited EOG-related biometric literature. According to the promising results, improved and high recognition accuracies (ACC/Accuracy: ≥99.99% and AUC/Area under the Curve: 0.99) have been achieved in addition to the high TAR (true acceptance rate) scores (≥98%) and low FAR (false acceptance rate) scores (≤3.33%) in seven individuals. On the other hand, authentication and monitoring for online users/students are becoming essential and important tasks due to the increase of the digital world (e-learning, e-banking, or e-government systems) and the COVID-19 pandemic. Especially in order to ensure reliable access, a highly scalable and affordable approach for authenticating the examinee without cheating or monitoring high-data-size video streaming is required in e-learning platforms and online education strategies. Hence, this work may present an approach that offers a sustainable, continuous, and reliable EOG-biometric authentication of digital applications, including e-learning platforms for users/students.

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