Deep secure PalmNet: A novel cancelable palmprint template protection scheme with deep attention net and randomized hashing security mechanism

Abstract

Overcoming challenges in the current cancelable palmprint template is essential for enhancing privacy and security in biometric systems, as these templates exhibit vulnerabilities in both template security and recognition performance. To address these challenges, we present a novel cancelable palmprint template protection scheme with a deep attention net and randomized hashing security mechanism. Firstly, to enhance recognition performance, we designed a deep attention net that is integrated into the model, aiming to improve its feature extraction ability. Additionally, in recognition of the paramount importance of security and privacy, we introduce a randomized hashing security mechanism. This mechanism, incorporating chaotic sequences as weights in a neuron activation layer, appends to the model and enables dynamic control of neuron activation while generating diverse palmprint feature templates. Subsequently, it enhances security and privacy by combining a Logistic-Tent-Sine (LTTS) random key with palmprint feature values through matrix multiplication. Furthermore, to optimize efficiency, particularly with a large dataset, a binarization layer is implemented using the Straight-Through Estimation (STE) algorithm. This layer contributes to computational efficiency and expedient data processing, further improving the performance of our palmprint template protection. The experimental results validate the outstanding accuracy of this scheme on TJU and PolyU palmprint datasets, establishing it as a state-of-the-art solution with remarkable recognition performance. Moreover, security analysis confirms its compliance with cancelable biometric template protection criteria, ensuring superior irreversibility, unlinkability, revocability, and privacy against various attacks.