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IEEE Final Year Project Topic for CSE

Base Paper Title

Securing Deep Learning Based Edge Finger-vein Biometrics with Binary Decision Diagram

Our Title

IEEE Project 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. Machine/deep learning based edge biometric systems outperform their non-machine learning counterpart. 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. This information leakage is not tolerable for biometric systems because biometric data in the original (raw)templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing but unsolved issue for deep learning based biometric recognition. To address the issue, in this paper we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capableof creating a new non-invertible version of the original finger-vein template, 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 ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the 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.

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