CBRW: A Novel Approach for Cancelable Biometric Template Generation based on
This work addresses security challenges in biometric systems by providing a method to protect biometric data, though it appears incremental as it builds on existing cancelable biometric techniques.
The paper tackles the problem of securing biometric images by proposing two novel cancelable biometric template generation methods based on Random Walk (CBRW-BitXOR and CBRW-BitCMP), which transform original biometrics into irreversible templates. The results show that these methods outperform state-of-the-art approaches on eight datasets, with superior performance in qualitative and quantitative metrics such as Correlation Coefficient and PSNR.
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.