  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.
