Biometric Authentication Using Face Thermal Images Based on Neural Fuzzy Extractor

• Published in: 2023 Intelligent Methods, Systems, and Applications (IMSA) pp. 80 - 85
• Main Authors: Sulavko, Alexey, Panfilova, Irina, Samotuga, Alexander, Zhumazanova, Samal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.07.2023
• Subjects: Authentication; automatic machine learning; Biological system modeling; biometric template protection; Biometrics (access control); deep convolutional networks; fuzzy extractor; information security; Neural networks; Passwords; shallow connected neural networks; Training; Transfer learning
• DOI: 10.1109/IMSA58542.2023.10217752

A method of biometric authentication based on the thermogram of the subject's face was proposed. This method allows you to associate a biometric image of a person with a cryptographic key or password, as well as protect the biometric image and key (password) from being compromised during storage and transmission over communication channels. This effect was achieved through the use of a fuzzy neural extractor trained according to the GOST R 52633.5 standard. The solution also uses a deep convolutional neural network for face detection and an Inception-Resnet network for feature embedding. RetinaFace, ResNet50 and VGG-Face were tested as alternatives to these neural network models. The best result achieved was EER = 4.91