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.

翻译：可撤销生物特征是一个具有挑战性的研究领域，通过将原始生物特征变换到另一个不可逆域来确保其安全性。文献中已提出多种生成可撤销生物特征模板的方法。本文提出了两种新颖且简洁的基于随机游走的可撤销生物特征模板生成方法（CBRW）。通过采用随机游走及所提出的两种算法（CBRW-BitXOR和CBRW-BitCMP）中的其他步骤，原始生物特征被转换为可撤销模板。所提方法的性能与现有最优方法进行了比较。实验在八个公开的灰度与彩色数据集上进行，包括CP（耳朵）（灰度与彩色）、UTIRIS（虹膜）（灰度与彩色）、ORL（人脸）（灰度）、IIT德里（虹膜）（灰度与彩色）以及AR（人脸）（彩色）。生成模板的性能通过相关系数（Cr）、均方根误差（RMSE）、峰值信噪比（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）等指标衡量。实验结果表明，所提方法在定性与定量分析中均优于现有最优方法。此外，CBRW在灰度与彩色图像上均表现出更优性能。