Fusing structure and color features for cancelable face recognition
Fusing structure and color features for cancelable face recognition
复制标题
融合结构和颜色特征以实现可取消的人脸识别
DOI：
10.1007/s11042-020-10234-8
复制
发表时间：
2021
影响因子：
3.6
通讯作者：
Liu Tie
中科院分区：
文献类型：
--
作者：
Xu Zihan;Zhuhong Shao;Yuanyuan Shang;Bicao Li;Hui Ding;Liu Tie
基于人脸的生物特征识别在当今被广泛使用，其中大量的人脸图像通常存储在第三方服务器上。由于人脸图像中包含了个人的敏感信息，如年龄和健康状况，因此有必要保护其隐私和安全。研究了一种可取消的彩色人脸模板保护算法。为了充分利用四元数表示，将局部方差和梯度等结构信息分别作为四元数表示的真实的部分。为了实现可重构性和可重分布性，采用了二进制矩阵的随机置换策略。然后采用基于四元数的二维主成分分析方法进行特征提取。有了它们，极端学习机器可以被训练并用于识别。在四个不同颜色的人脸数据集上进行的实验结果表明，结构信息的融合可以大大提高准确率。更重要的是，随机排列不仅不会降低识别精度，而且保证了人脸模板的安全性和可撤销性。
Face-based biometric recognition is widely used nowadays, where substantial face images are commonly stored on third-party servers. Since the sensitive information of an individual is contained in facial image such as the age and health condition, it is necessary to protect its privacy and security. This paper investigates a cancelable color face template protection algorithm. To make full use of quaternion representation, the structural information including local variance and gradient is respectively served as the real part. To achieve revocability and ability to redistribute, the strategy of random permutation with binary matrix is adopted. Afterwards, the quaternion-based two-dimensional principal component analysis is employed to extract features. With them, the extreme learning machine can be trained and used for recognition. Experimental results performed on four different color face datasets have demonstrated that the fusion of structural information can greatly improve the accuracy. More importantly, the random permutation not only does not reduce the recognition accuracy, but also guarantees the security and revocation of face template.