SIRSE: A secure identity recognition scheme based on electroencephalogram data with multi-factor feature. (January 2018)

• Record Type: Journal Article
• Authors: Liang, Wei; Tang, Mingdong; Jing, Long; Sangaiah, Arun Kumar; Huang, Yin
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 310
• Page End: 321
• Publication Date: 2018-01
• Subjects: Brainwave -- EEG -- Machine learning -- Identity recognition; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.05.001
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The use of EEG signal in secure authentication is effective to improve security of identity recognition. A secure identity recognition approach based on multi-factor feature electroencephalogram (EEG) is proposed. The proposed scheme not only realizes anti-counterfeiting and bio-assay, but also makes up security vulnerabilities of traditional identity recognition techniques based on biological features. Abstract: As a common bioelectric phenomenon, brainwave is produced by electric field change in human brain. The use of electroencephalogram (EEG) in identity recognition greatly enhances recognition security and accuracy. In this work, we propose a secure identity recognition method by using EEG with multi-factor feature. Machine learning is utilized to extract, classify and select features of EEG data. Structural sparse norm in EEG data has consistency. The feature is helpful in optimization. The experiments show that SIRSE has superiority on security, recognition success rate and reliability in comparison. Graphical abstract: