Abstract
Cancelable Biometric is repetitive distortion embedded in original Biometric image for keeping it secure from unauthorized access. In this paper, we have generated Cancelable Biometric templates with Reverse Boolean XOR technique. Three different methods have been proposed for generation of Cancelable Biometric templates based on Visual Secret Sharing scheme. In each method, one Secret image and n-1 Cover images are used as: (M1) One original Biometric image (Secret) with n- 1 randomly chosen Gray Cover images (M2) One original Secret image with n-1 Cover images, which are Randomly Permuted version of the original Secret image (M3) One Secret image with n-1 Cover images, both Secret image and Cover images are Randomly Permuted version of original Biometric image. Experiment works have performed on publicly available ORL Face database and IIT Delhi Iris database. The performance of the proposed methods is compared in terms of Co-relation Coefficient (Cr), Mean Square Error (MSE), Mean Absolute Error (MAE), Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), Number of Pixel Change Rate (NPCR), and Unified Average Changing Intensity (UACI). It is found that among the three proposed method, M3 generates good quality Cancelable templates and gives best performance in terms of quality. M3 is also better in quantitative terms on ORL dataset while M2 and M3 are comparable on IIT Delhi Iris dataset.
Abstract (translated)
可取消生物特征是在原始生物特征图像中重复扭曲以保持其安全性，以防止未经授权的访问。在本文中，我们使用反向布尔异或技术生成了可取消生物特征模板。根据视觉共享方案，有三种不同的方法提出了生成可取消生物特征模板的方法。每种方法都使用一个秘密图像和n-1个随机的灰度覆盖图像：（M1）一个n-1个随机选择的灰度覆盖图像的秘密图像（M2）一个n-1个随机选择的与原始秘密图像的随机排列版本的原始秘密图像（M3）一个带有n-1个随机选择的灰度覆盖图像的原始生物特征图像（M4）一个带有n-1个随机选择的与原始生物特征图像的随机排列版本的秘密图像。在公开可用的ORL人脸数据库和IIT德里Iris数据库上进行了实验。所提出方法的性能在相关系数系数（Cr）、平均平方误差（MSE）、平均绝对误差（MAE）、结构相似性（SSIM）、峰信号比噪声比（PSNR）和像素变化率（NPCR）以及统一平均变化强度（UACI）方面进行了比较。实验结果表明，在三种提出的方法中，M3生成的可取消生物特征模板质量最好，并且在质量方面具有最佳性能。M3在ORL数据集上的定量表现优于M2和M3在IIT德里Iris数据集上的表现。
URL
https://arxiv.org/abs/2404.15394
