VERIFICATION OF DYNAMIC BIOMETRIC PARAMETERS OF A PERSONALITY BASED ON A PROBABLE NEURAL NETWORK

Abstract

Biometric identity verification is used primarily for access to computer and mobile systems, as well as for remote (voice) verification. In fact, the most widespread systems are biometric verification systems based on a fixed passphrase, which are quite simple to implement, but very vulnerable to attacks of reproduction of a compromised short text. To eliminate this drawback, it is proposed to carry out identity verification using a text that is arbitrary in terms of volume, content and language (text-independent biometric verification). This paper proposes a generalized approach to solve the problem of identity verification by dynamic biometric parameters of different modality (keyboard writing, handwriting, voice). The presentation of dynamic biometrics signals is carried out by converting them into a sequences of information units, each of which contains the same number of counts of biometric signal of corresponding modality. The solution to this problem is carried out by monitoring the degree of concentration of closely located information units (clusters) at certain points of the multidimensional feature space. Such control is implemented on a probabilistic neural network thatstatistically evaluates the probability density of the distribution of information units in the corresponding clusters with the subsequent determination of the total probability density for the entire class of objects. The advantages of the proposed approach are: generalization of substantially different methods of text-independent identity verification by dynamic biometric parameters of different modality; the ability to make a verification decision for a fixed time of receipt of biometric data, determined by the size of the model used; the ability to set the verification accuracy by changing the dimension of the layer of probabilistic network samples. The disadvantage of the proposed approach is the need for software implementation of a large-scale neural network. However, this drawback is quickly leveled with an increase in the productivity of computer technology.