Securing deep learning based edge finger-vein biometrics with binary decision diagram

Authors/Creators

Wencheng Yang, Edith Cowan UniversityFollow
Song Wang
Jiankun Hu
Guanglou Zhang, Edith Cowan UniversityFollow
Jucheng Yang
Craig Valli, Edith Cowan UniversityFollow

Abstract

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. To address the issue, in this paper we develop a novel biometric template protection algorithm using the binary decision diagram (BDD), which is stacked with an artificial neural network - the multi-layer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Document Type

Journal Article

Date of Publication

2019

Publication Title

IEEE Transactions on Industrial Informatics

Publisher

IEEE

School

School of Science / Security Research Institute

RAS ID

28303

Comments

Yang, W., Wang, S., Hu, J., Zheng, G., Yang, J., & Valli, C. (2019). Securing deep learning based edge finger-vein biometrics with binary decision diagram. IEEE Transactions on Industrial Informatics. 15(7) 4244-4253. Available here

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