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Article title TEXT-INDEPENDENT PERSONALITY IDENTIFICATION WITH THE USE OF DYNAMIC BIOMETRIC PARAMETERS BASED ON THE IMMUNE MODEL OF CLONAL SELECTION
Authors Yu. A. Bryuhomitsky
Section SECTION II. MODELING OF COMPLEX SYSTEMS AND PROCESSES
Month, Year 05, 2018 @en
Index UDC 004.067
DOI
Abstract An immunological approach is proposed to solve the problem of recognition of dynamic biometric signals, based on the principles of massively parallel decentralized data processing used in artificial immune systems. A special feature of approach is presentation of dynamic biometrics signals by sequences of informational units of a certain format, with their following RTP processing on the basis of the immunological clonal selection model with positive selection. As an informational units are syntactically related fragments of the text of the corresponding modality. They are represented by multidimensional vectors in the workspace of features. In the training phase, an initial population of detectors is created in the metric of the vectors of the investigated sequence of biometric data. Then, according to the principle of positive selection, the detectors of the initial population are identified, which in the feature space are closest to the areas of distribution of the corresponding biometric data. The proximity of the vectors in the feature space models the property of the affinity of the immune system cells. Revealed detectors, using the iterative procedure, are subjected to cloning, hypermutation and selection and ultimately form a population of immune memory detectors. The training procedure is stopped when the specified maximum size of the population of the immune memory detectors is reached.In the recognition phase, the elements of the analyzed biometric data sequence are compared with the memory population detectors using the Euclid proximity measure. The critical level of proximity defines the boundary for the decision "well-known/stranger" making by the system and is specified based on the permissible errors of the first kind. To identify "well-known/stranger", a statistical approach is used, in which the frequency of the critical proximity condition is controlled, which determines the statistical probability of belonging to the analyzed biometry to "stranger". The proposed approach within the framework of the immunological presentation allows to generalize essentially different methods of personality identification by the dynamic biometric parameters of different modalities. The positive differences of the proposed approach are: the possibility of text-independent analysis of dynamic biometrics of any modality, arbitrary volume and content; continuous assessment of the biometric data in RTP with the possibility of timely decision-making on the presence of "stranger"; the use of an immunological model that fits well with most of the tasks of dynamic personality identification, which allows to reduce significantly the number of detectors required for effective identification of a person.

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Keywords Text-independent personality identification; dynamic biometry; artificial immune systems; vector data representation; immune model of clonal selection.
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