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.