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.

提出了两种基于随机游走的新颖简单的可取消生物特征模板生成方法 CBRW-BitXOR 和 CBRW-BitCMP，通过将原始生物特征转换为可取消模板。与其他最先进的方法进行比较，证明了所提方法在定性和定量分析方面均优于其他方法，且 CBRW 在灰度图像和彩色图像上都表现更好。

CBRW: 基于可取消生物特征模板生成的新方法

CBRW: A Novel Approach for Cancelable Biometric Template Generation  based on

Implicit Neural Representation (INR) has recently attracted considerable
attention for storing various types of signals in continuous forms. The
existing INR networks require lengthy training processes and high-performance
computational resources. In this paper, we propose a novel sub-optimal ensemble
architecture for INR that resolves the aforementioned problems. In this
architecture, the representation task is divided into several sub-tasks done by
independent sub-networks. We show that the performance of the proposed ensemble
INR architecture may decrease if the dimensions of sub-networks increase.
Hence, it is vital to suggest an optimization algorithm to find the sub-optimal
structure of the ensemble network, which is done in this paper. According to
the simulation results, the proposed architecture not only has significantly
fewer floating-point operations (FLOPs) and less training time, but it also has
better performance in terms of Peak Signal to Noise Ratio (PSNR) compared to
those of its counterparts.

本文提出了一种新的 INR 子优化集成架构来解决现有的训练过程冗长和计算资源占用高问题，该架构将表示任务分解成几个独立子网络完成，并建立了优化算法以找到子网络的最优结构。仿真结果表明，该架构具有更少的浮点运算次数和更短的训练时间，并在峰值信噪比方面具有比同类产品更好的性能。