Authors: Isao Nakanishi; Sadanao Baba; Koutaro Ozaki; Shigang Li
Addresses: Graduate School of Engineering, Tottori University, 4-101 Koyama-minami, Tottori 680-8552, Japan ' Graduate School of Engineering, Tottori University, 4-101 Koyama-minami, Tottori 680-8552, Japan; JTEKT Corporation, 4-7-1 Meieki, Nakamura-ku, Nagoya 450-8515, Japan ' Faculty of Engineering, Tottori University, 4-101 Koyama-minami, Tottori 680-8552, Japan; SUZUKI MOTOR Corporation, 300 Takatsuka, Minami-ku, Hamamatsu 432-8611, Japan ' Graduate School of Engineering, Tottori University, 4-101 Koyama-minami, Tottori 680-8552, Japan
Abstract: Conventional biometric systems mainly assume one-time-only authentication. However, this technique is not used with user management applications. If a user is replaced by an imposter after the authentication has occurred, the systems cannot detect such a replacement. One solution to this problem is on-demand authentication, in which users are authenticated on a regular or non-regular schedule, as determined by the system. However, the on demand-authentication technique requires that we present biometric data without regard to do so. In this paper, we focus on the use of brain waves as transparent biometric signals. In particular, we assume driver authentication and measure the brain waves of drivers when they are performing mental tasks such as tracing routes or using a simplified driving simulator as an actual task. We propose to extract the power spectrum in the α-ß band as an individual feature and propose two verification methods based on the similarity of the features. In addition, we propose to divide the α-ß band into either four or six partitions and to fuse the similarity scores from all the partitions. We evaluate the verification performance using 23 subjects and obtain an equal error rate of 20-25%.
Keywords: brain waves; EEG; on-demand authentication; transparent biometrics; driver authentication; route tracing; simplified driving simulator; electroencephalograms.
International Journal of Biometrics, 2013 Vol.5 No.3/4, pp.288 - 305
Available online: 22 Aug 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article