Abstract
Cancelable Biometric is a challenging research field in which security of an original biometric image is ensured by transforming the original biometric into another irreversible domain. Several approaches have been suggested in literature for generating cancelable biometric templates. In this paper, two novel and simple cancelable biometric template generation methods based on Random Walk (CBRW) have been proposed. By employing random walk and other steps given in the proposed two algorithms viz. CBRW-BitXOR and CBRW-BitCMP, the original biometric is transformed into a cancellable template. The performance of the proposed methods is compared with other state-of-the-art methods. Experiments have been performed on eight publicly available gray and color datasets i.e. CP (ear) (gray and color), UTIRIS (iris) (gray and color), ORL (face) (gray), IIT Delhi (iris) (gray and color), and AR (face) (color). Performance of the generated templates is measured in terms of Correlation Coefficient (Cr), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Mean Absolute Error (MAE), Number of Pixel Change Rate (NPCR), and Unified Average Changing Intensity (UACI). By experimental results, it has been proved that proposed methods are superior than other state-of-the-art methods in qualitative as well as quantitative analysis. Furthermore, CBRW performs better on both gray as well as color images.
Abstract (translated)
可取消生物特征是一个具有挑战性的研究领域，其中通过将原始生物特征变换为另一个不可逆的领域来确保原始生物特征的安全。在文献中，已经提出了几种生成可取消生物特征模板的方法。本文提出了两种基于随机漫步（CBRW）的新颖且简单的可取消生物特征模板生成方法。通过使用提出的两种算法 viz. CBRW-BitXOR 和 CBRW-BitCMP，将原始生物特征变换为可取消模板。本文方法的表现与最先进的算法进行了比较。实验在八个公开可用的灰度和彩色数据集上进行，即CP（耳朵）（灰度与彩色），UTIRIS（眼睛）（灰度与彩色），ORL（面部）（灰度），IIT德里亚（眼睛）（灰度与彩色），和AR（眼睛）（彩色）。生成模板的表现用相关系数系数（Cr）、算术均方误差（RMSE）、峰值信号与噪声比（PSNR）、结构相似性（SSIM）、平均绝对误差（MAE）、像素变化率（NPCR）和统一平均变化强度（UACI）进行衡量。通过实验结果，证明了本文方法在质量和数量分析方面优于其他最先进的算法。此外，CBRW在灰度和彩色图像上表现更好。
URL
https://arxiv.org/abs/2404.16739
