Cancelable Biometric is repetitive distortion embedded in original Biometric
image for keeping it secure from unauthorized access. In this paper, we have
generated Cancelable Biometric templates with Reverse Boolean XOR technique.
Three different methods have been proposed for generation of Cancelable
Biometric templates based on Visual Secret Sharing scheme. In each method, one
Secret image and n-1 Cover images are used as: (M1) One original Biometric
image (Secret) with n- 1 randomly chosen Gray Cover images (M2) One original
Secret image with n-1 Cover images, which are Randomly Permuted version of the
original Secret image (M3) One Secret image with n-1 Cover images, both Secret
image and Cover images are Randomly Permuted version of original Biometric
image. Experiment works have performed on publicly available ORL Face database
and IIT Delhi Iris database. The performance of the proposed methods is
compared in terms of Co-relation Coefficient (Cr), Mean Square Error (MSE),
Mean Absolute Error (MAE), Structural Similarity (SSIM), Peak Signal to Noise
Ratio (PSNR), Number of Pixel Change Rate (NPCR), and Unified Average Changing
Intensity (UACI). It is found that among the three proposed method, M3
generates good quality Cancelable templates and gives best performance in terms
of quality. M3 is also better in quantitative terms on ORL dataset while M2 and
M3 are comparable on IIT Delhi Iris dataset.

生成可取消生物特征模板，采用逆布尔异或技术，在三种不同方法中，以视觉秘密共享方案为基础，使用一个秘密图像和 n-1 个覆盖图像。通过在公开可获得的 ORL 人脸数据库和 IIT 德里虹膜数据库上的实验证明，提出的方法在相关系数、均方差、均方差、结构相似性、峰值信噪比、像素变化率和统一平均变化强度等方面进行了比较，发现在质量方面，方法 M3 生成了良好的可取消模板，并在 ORL 数据集上具有最佳性能，在 IIT 德里虹膜数据集上，M2 和 M3 具有可比性。