Shigang Li

Evaluation of Brain Waves as Biometrics for Driver Authentication Using Simplified Driving Simulator

The brain wave is able to present biometric data unconsciously, so that it enables continuous or on-demand authentication which is effective in user management. In this paper, assuming an application to driver authentication, we evaluate verification performance of the brain wave using a simplified driving simulator. In addition, dividing the

Using brain waves as transparent biometrics for on-demand driver authentication

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Performance Evaluation of Intra-palm Propagation Signals as Biometrics

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

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Brain waves as biometrics in relaxed and mentally tasked conditions with eyes closed

band to several partitions, we propose to extract the difference between a mean value of the power spectrum at each partition in relaxed condition and that in mental-tasked condition as an individual feature. Fusing the differences from higher partitions in verification, the EER of 24% is obtained among 10 subjects.

DWT Domain On-Line Signature Verification

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–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%.

Lane departure estimation by side fisheye camera

The use of intra-palm propagation signals as biometrics is proposed. The intra-palm propagation signal is an electromagnetic wave propagated in the palm. In this study, intra-palm propagation signals are measured using dedicated measuring devices and their verification performance based on the Support Vector Machine is evaluated using twenty-one subjects. The equal error rate is approximately 25 %.

Biometric verification using brain waves toward on-demand user management systems: performance differences between divided regions in α -- β wave band

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.

Speech Enhancement Based on Frequency Domain ALE with Adaptive De-Correlation Parameters

In this study, we investigate the practical application of brain wave biometrics to operator authentication of a system where the operator wears a brain wave sensor, and is authenticated while using the system. The verification performance is examined for subjects under the eye-closed and relaxed condition, and the eye-closed and mentally tasked condition. In the latter case, we assume biometric verification of computer users, and we adopt a mental task that we call mental composition. In addition, this application is made more practical by using an electroencephalograph that has a single electrode. Further, we propose simple feature extraction and verification based on spectral information. Our experiments on a set of approximately 20 subjects yield an equal error rate (EER) of approximately 15%.

Interpolation of discrete spherical image

Biometrics attracts attention since person authentication becomes very important in networked society. As the biometrics, the fingerprint, iris, face, ear, vein, gate, voice and signature are well known and are used in various applications (Jain et al., 1999; James et al., 2005). Especially, assuming mobile access using a portable terminal such as a personal digital assistant (PDA), a camera, microphone, and pen-tablet are normally equipped; therefore, authentication using the face, voice and/or signature can be realized with no additional sensor.