Fingerprint bio‐key generation based on a deep neural network
Zhendong Wu, Zhengyin Lv, Jie Kang, Wenqian Ding, Jianwu Zhang
Abstract
With the increasing use of biometric identity authentication, biological key generation technology is receiving much attention. A high-strength key that is easy to store and manage can be generated from biological characteristics, which can improve the convenience and security of user-encryption operations. However, the generation of a high-strength, stable, and robust key using the currently available fingerprint bio-key generation technology is difficult. This paper proposes a three-layer framework for fingerprint bio-key generation that is composed of a fingerprint bio-key preprocessor, fingerprint bio-key stabilizer (FPBK_Stabilizer), and fingerprint bio-key fuzzy extractor. In the FPBK_Stabilizer, feature selection and layer-by-layer convolution projection characteristics from deep neural networks are used to effectively eliminate the instability between fingerprint samples. Furthermore, a suitable multilayer convolutional projection fingerprint bio-key generation model is designed for generating the fingerprint bio-key. The results of a fingerprint bio-key generation experiment involving a fingerprint library comprising 100 people verified the efficacy of the proposed framework. Specifically, the proposed framework exhibited a generation intensity >1024 bits, accuracy rate >98.0%, and misrecognition rate <1.5%, thereby verifying its high-strength, stable, and robust fingerprint bio-key generation capability.