A Secure Biometric Key Generation Based on Coordinate Attention Mechanism And Reliable Feature Selection

Abstract

With the development of the information era, the study of biometric keys has attracted increasing attention. However, the performance and safety of the proposed methods seem challenging to meet the needs of real applications. To solve this problem, this paper presents a finger vein biometric key system. The proposed system proposes an improved network in the feature extraction stage, which combines the coordinate attention mechanism and SkipNet to obtain richer feature information. An improved, reliable feature selection method is introduced to acquire more discriminative finger vein features. A Lagrange interpolation method then binds the user's finger vein features and keys. Finally, experiments are carried out on the public USM, Poly, and Net64 databases. The results indicate that we acquire a 1024-bit key with 0.049% FAR and 0% FRR in Net64 and 0% FAR and FRR in USM and Poly. Additionally, the system's security is analyzed from key randomness, system information leak attack, brute force attack, cross-matching attack, and spoofing attack. Experimental and theoretical analyses show that the proposed system has good accuracy and security.