Bayes-Minkowski measure and building on its basis immune machine learning algorithms for biometric facial identification
Year: 2020
Type: conference-paper
Abstract: 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.
Author: A E Sulavko
Institution: Omsk State Technical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Award: G8232428549
OpenAlex ID: W3029675009