User Authentication by Face Thermograms Based on Hybrid Neural Networks

For the security of large information systems, biometric subject authentication systems are widely used. To ensure the fault tolerance of the biometric system, the most promising model of the "biometrics-to-code" converter is based on artificial neural networks. At the same time, it is imp...

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Bibliographic Details
Main Authors: Zhumazhanova, S. S., Tatarinov, I. D.
Format: Conference Proceeding
Language:English
Subjects:
Authentication
Bayesian functionals
Biological system modeling
biometric authentication
Biometrics (access control)
biometrics-to-code converters
Containers
hybrid networks
Neurons
Solid modeling
thermal images
Training
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Summary:For the security of large information systems, biometric subject authentication systems are widely used. To ensure the fault tolerance of the biometric system, the most promising model of the "biometrics-to-code" converter is based on artificial neural networks. At the same time, it is important to investigate the influence of the psychophysiological state (PPS) of users on the results of their authentication. In this paper, we consider a hybrid model of a neural network "biometrics-to-code" converter (based on a new type of hybrid neural networks) that does not compromise the biometric template and the user's key (password) and is resistant to attacks of knowledge extraction, developed for biometric authentication of using thermographic images of users' face and neck in 7 PPS.
ISSN:2644-2760
DOI:10.1109/Dynamics52735.2021.9653694