SVM-Based Biometric Authentication Using Intra-Body Propagation Signals

To use intra-body propagation signals for biometric authentication have been proposed. The intra-body propagation signals are hid in human bodies; therefore, they have tolerability to circumvention using artifacts. Additionally, utilizing the signals in the body enables liveness detection with no additional scheme. The problem is, however, verification performance using the intra-body propagation signal is not so high. In this paper, in order to improve the performance we propose to use user-specific frequency bands for all users in verification. The verification performance is improved to 70 %. Furthermore, we introduce the support vector machine (SVM) into the verification process. It is confirmed that verification rate of about 86 % is achieved

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

Performance Evaluation of Intra-Palm Propagation Signals as Biometrics

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 %