Securing deep learning based edge finger-vein biometrics with binary decision diagram
IEEE Transactions on Industrial Informatics
School of Science / Security Research Institute
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.