Open AccessBayes-Minkowski measure and building on its basis immune machine learning algorithms for biometric facial identification
Author(s) -
A. E. Sulavko
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1546/1/012103
Subject(s) - biometrics , identification (biology) , measure (data warehouse) , bayes' theorem , algorithm , minkowski space , artificial intelligence , computer science , naive bayes classifier , machine learning , pattern recognition (psychology) , face (sociological concept) , support vector machine , mathematics , data mining , bayesian probability , social science , botany , geometry , biology , sociology
In this paper we propose a new Bayes-Minkowski proximity measure that can be used to process correlated biometric, biomedical, and other type of data (with the normal distribution law or close to it). The Bayes-Minkowski measure is an antagonist criterion with respect to the Minkowski measure, since it shows opposite properties. It is possible to build a hybrid network of classifiers and apply immune learning algorithms to the network based on these proximity measures. It was demonstrated in the work on the example of tasks of identification and verification of a person’s personality by facial image. The achieved errors probabilities of person’s identification and verification by face features were: 0029 и 0.0017, respectively.