CONTROL AND DECISION-MAKING
CONTROL SYSTEMS
S. A. Ilyuhin, D. V. Polevoy, T. S. Chernov Improving the Accuracy of Neural Network Methods of Verification of Persons by Spatial-Weighted Normalization of Brightness Image
SOFTWARE ENGINEERING
DATA PROCESSING AND ANALYSIS
PATTERN RECOGNITION
SECURITY ISSUES
S. A. Ilyuhin, D. V. Polevoy, T. S. Chernov Improving the Accuracy of Neural Network Methods of Verification of Persons by Spatial-Weighted Normalization of Brightness Image

Abstract.

In this article, we propose a method of spatially weighted brightness normalization for facial grayscale images which retains more information during the normalization process. An experimental study is being conducted of the effect of various brightness normalization options on the accuracy of a fixed neural network classifier in the verification problem. It is experimentally shown that the proposed brightness normalization can improve the accuracy of facial images verification in complex lighting conditions and compensate for the samples that were not present in the training data.

Keywords:

face verification, cross-domain biometrics, brightness normalization, image processing.

PP. 12-20.

DOI 10.14357/20718632190402

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