一种基于噪声放大的可消除生物特征识别算法

Abd El-Rahman Farouk Al-Libady, Huda Ibrahim mohamed Ashiba, Ghada M. El banby, A. El-Fishawy, M. Dessouky, F. A. Abd El-Samie, El-Sayed M. El-Rabaie
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引用次数: 0

摘要

最近,生物识别系统在现代安全应用中得到了广泛应用。不幸的是,这些系统经历了几次黑客攻击。如果生物特征数据库被破坏和窃取,这些数据库中的生物特征将永远丢失。因此,迫切需要引入新的可升级的生物识别系统。可取消生物识别技术背后的概念是将生物识别数据转换为替代模板,这些模板不能被冒名顶替者或入侵者轻易使用,如果被破坏,可以被消除。本文将逆滤波器应用于可取消人脸识别系统中。该系统通过模糊、加噪、反滤波等方法生成蒙面生物特征图像。在图像处理理论中,众所周知,反滤波会导致噪声增强,这是图像恢复中不希望出现的效果。相反,这种效果在可取消的生物识别系统中是理想的。如果将噪声放大到适当的程度,它可以掩盖原始的生物特征,从而导致可取消的模板。这就是所提议的系统背后的理论。该系统在ORL和Olivetti数据集上得到了应用。采用不可逆性、不可链接性、目视检测、假阳性率(FPR)、假阴性率(FNR)、等错误率(EER)、可判决性、相关系数、接收机工作特性曲线下面积(AROC)等评价指标的仿真结果表明,该系统具有良好的抗干扰能力。因此,它对一些安全应用程序是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Efficient Algorithm for Cancelable Biometric Recognition Based on Noise Magnification
More recently, biometric systems have spread for modern security applications. Unfortunately, these systems have experienced several attempts of hacking. If biometric databases are compromised and stolen, biometrics in these databases will be lost forever. Consequently, there is an immediate need to introduce new upgradable biometric systems. The concept behind cancelable biometrics is to convert biometric data to alternative templates, which cannot be easily used by the impostor or intruder, and can be eliminated if breached. In this paper, the inverse filter is utilized in a cancelable face recognition system. In this system, masked biometric images are generated by blurring, noise addition and then inverse filtering. It is well-known in the image processing theory that inverse filtering leads to noise enhancement, which is an undesired effect in image restoration. In contrary, this effect will be desired in cancelable biometric systems. If the noise is magnified with an appropriate extent, it can mask the original biometrics leading to cancelable templates. This is the theory behind the proposed system. The proposed system is applied on the Olivetti and Oracle (ORL) dataset. Simulation results using evaluation metrics such as non-invertibility, unlinkability, visual inspection, False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Decidability, correlation coefficient, Area under the Receiver Operating Characteristic (AROC) curve demonstrate that the proposed system is resistant to intruders and hackers. Hence, it is efficient for several security applications.
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