On-Demand Biometric Authentication of Computer Users Using Brain Waves

From the viewpoint of user management, on-demand biometric authentication is effective for achieving high-security. In such a case, unconscious biometrics is needed and we have studied to use a brain wave (Electroencephalogram: EEG). In this paper, we examine the performance of verification based on the EEG during a mental task. In particular, assuming the verification of computer users, we adopt the mental task where users are thinking of the contents of documents. From experimental results using 20 subjects, it is confirmed that the verification using the EEG is applicable even when the users are doing the mental task

Driver authentication using brain waves while route tracing as a mental task

From the viewpoint of user management, continuous or on-demand biometric authentication is effective for achieving higher security. In such a case, the biometrics which is able to present biometric data unconsciously is needed and we have proposed to use the brain wave as the unconscious biometrics. In this paper, assuming driver authentication, we measure brain waves of drivers when they are tracing routes as a mental task. And we evaluate verification performance using the difference between the mean power spectrum at α-β band in relaxed condition and that in mental-tasked condition as an individual feature. As a result, the EER of 31 % is obtained among 12 subjects

Unconscious Biometrics for Continuous User Verification

In user management system, continuous or successive (ondemand) authentication is required to prevent identity theft. In particular, biometrics of which data are unconsciously presented to authentication systems is necessary. In this paper, brain waves and intra-palm propagation signals are introduced as biometrics and their verification performances using actually measured data are presented

User verification based on the support vector machine using intra-body propagation signals

Use of intra-body propagation signals has been proposed for biometric authentication. However, verification performance of the conventional method is low. To overcome this limitation, this study introduces the support vector machine (SVM) into the verification process, which improves the verification rate to approximately 83%. However, the correct acceptance rate of genuine users using only SVM is 49%, which is too low for practical applications. Thus, we introduce the concept of one versus one (1vs1) SVM. Each 1vs1 SVM distinguishes a genuine (authorised) user from another (unauthorised) user. Verification is achieved on the basis of a majority rule using plural 1vs1 SVMs related to a genuine user. The correct acceptance rate is greatly improved to 84% while maintaining equivalent verification performance. As a result, it is further confirmed that an intra-body propagation signal is a potential new biometric trait

Using BrainWaves as Transparent Biometrics for On-Demand Driver Authentication

Conventional biometric systems mainly assume one-timeonly 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 nonregular 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–β 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–β 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 %