Biometric authentication on the basis of lectroencephalograms parameters

Abstract

Static biometric patterns such as fingerprint, iris and face are difficult to keep secret. Since the open pattern has a little potential replacement options stealing a strange open biometrics provides great opportunities for compromising systems. Authentication on the basis of electroencephalogram pattern (EEG) is the most secure kind of biometric security. The present study aims to develop a method of biometric authentication by the EEG data with high accuracy. Several neural network EEG pattern verification algorithms have been tested. A method for verification of the human EEG pattern based on a modified Bayes hypothesis formula has been developed. The following error indicators FAR <10-4 with FRR = 0.062 were achieved. This work was supported by the Russian Foundation for Basic Research (RFBR) and the Government of the Omsk Region (grant 18-41-550002).

Biometric-Based Key Generation and User Authentication Using Acoustic Characteristics of the Outer Ear and a Network of Correlation Neurons

“…The informativeness level of a feature is an important indicator [ 21 ]. The amount of individual information of the j -th feature for a certain class of images is determined using Formula (3): I j = −log 2 ( AUC (Փ G ( a j ), Փ I ( a j ))), where AUC, the area under the curve, is limited by the probability density functions «Genuine» Փ G (a j ) and «Impostors» Փ I (a j ) , as well as by the x -axis.…”

“…The amount of individual information of the j -th feature for a certain class of images is determined using Formula (3): I j = −log 2 ( AUC (Փ G ( a j ), Փ I ( a j ))), where AUC, the area under the curve, is limited by the probability density functions «Genuine» Փ G (a j ) and «Impostors» Փ I (a j ) , as well as by the x -axis. Փ G ( a j ) characterizes the values of the feature strictly for a certain class of images, and Փ I (a j ) characterizes the values of the same feature for all classes of images as a whole [ 21 ]. The higher the I on average, the further separated the proper class regions in the feature space.…”