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一种基于混沌系统的不可逆且可撤销的模板生成方案。

An Irreversible and Revocable Template Generation Scheme Based on Chaotic System.

作者信息

Liu Jinyuan, Wang Yong, Wang Kun, Liu Zhuo

机构信息

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

出版信息

Entropy (Basel). 2023 Feb 18;25(2):378. doi: 10.3390/e25020378.

Abstract

Face recognition technology has developed rapidly in recent years, and a large number of applications based on face recognition have emerged. Because the template generated by the face recognition system stores the relevant information of facial biometrics, its security is attracting more and more attention. This paper proposes a secure template generation scheme based on a chaotic system. Firstly, the extracted face feature vector is permuted to eliminate the correlation within the vector. Then, the orthogonal matrix is used to transform the vector, and the state value of the vector is changed, while maintaining the original distance between the vectors. Finally, the cosine value of the included angle between the feature vector and different random vectors are calculated and converted into integers to generate the template. The chaotic system is used to drive the template generation process, which not only enhances the diversity of templates, but also has good revocability. In addition, the generated template is irreversible, and even if the template is leaked, it will not disclose the biometric information of users. Experimental results and theoretical analysis on the RaFD and Aberdeen datasets show that the proposed scheme has good verification performance and high security.

摘要

近年来,人脸识别技术发展迅速,出现了大量基于人脸识别的应用。由于人脸识别系统生成的模板存储了面部生物特征的相关信息,其安全性越来越受到关注。本文提出了一种基于混沌系统的安全模板生成方案。首先,对提取的人脸特征向量进行置乱,以消除向量内的相关性。然后,使用正交矩阵对向量进行变换,改变向量的状态值,同时保持向量之间的原始距离。最后,计算特征向量与不同随机向量夹角的余弦值并转换为整数以生成模板。利用混沌系统驱动模板生成过程,不仅增强了模板的多样性,还具有良好的可撤销性。此外,生成的模板是不可逆的,即使模板泄露,也不会泄露用户的生物特征信息。在RaFD和阿伯丁数据集上的实验结果和理论分析表明,该方案具有良好的验证性能和高安全性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/916e/9955787/f05c2f9c5943/entropy-25-00378-g001.jpg

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