Secure Authentication for Face Recognition
Secure Authentication for Face Recognition
复制标题
DOI：
10.1109/ciisp.2007.369304
复制
发表时间：
2007-04
期刊：
影响因子：
--
通讯作者：
M. Dabbah;W. L. Woo;S. Dlay
中科院分区：
文献类型：
--
作者：
M. Dabbah;W. L. Woo;S. Dlay
在本文中，我们提出了一种在识别过程中使用所谓的可撤销生物特征来保护面部生物特征的新技术。该技术基于使用二维主成分分析（2DPCA）算法的基于图像（统计）的人脸识别。生物特征数据使用多项式函数和共生矩阵转换到其可撤销域。原始面部图像通过一个多项式函数进行非线性变换，该多项式函数的参数可以根据安全可撤销模板的发布版本相应地改变。共生矩阵也用于变换中以生成一个独特的特征向量，该向量用于安全性和识别准确性。哈达玛积用于构建最终的可撤销模板。它在证明两个独立协方差矩阵之间的新关系方面显示出高度的灵活性，这在数学上已得到证明。生成的可撤销模板的使用方式与原始面部图像相同。二维主成分分析识别算法无需任何改变即可使用；变换仅应用于输入图像，但具有更高的识别准确性。理论和实验结果表明，数据具有高度的不可逆性，与原始数据相比，准确性提高了高达3%。
In this paper, we present a new technique to protect the face biometric during recognition, using the so called cancellable biometric. The technique is based on image-based (statistical) face recognition using the 2DPCA algorithm. The biometric data is transformed to its cancellable domain using polynomial functions and co-occurrence matrices. Original facial images are transformed non-linearly by a polynomial function whose parameters can be change accordingly to the issuing version of the secure cancellable template. Co-occurrence matrices are also used in the transform to generate a distinctive feature vector which is used for both security and recognition accuracy. The Hadamard product is used to construct the final cancellable template. It shows high flexibility in proving a new relationship between two independent covariance matrices, which is mathematically proven. The generated cancellable templates are used in the same fashion as the original facial images. The 2DPCA recognition algorithm has been used without any changes; the transformations are applied on the input images only and yet with higher recognition accuracy. Theoretical and experimental results have shown high irreversibility of data with improved accuracy of up to 3% from the original data