It has been shown that face images can be reconstructed from their representations (templates). We propose a randomized CNN to generate protected face biometric templates given the input face image and a user-specific key. The use of user-specific keys introduces randomness to the secure template and hence strengthens the template security. To further enhance the security of the templates, instead of storing the key, we store a secure sketch that can be decoded to generate the key with genuine queries submitted to the system. We have evaluated the proposed protected template generation method using three benchmarking datasets for the face (FRGC v2.0, CFP, and IJB-A). The experimental results justify that the protected template generated by the proposed method are non-invertible and cancellable, while preserving the verification performance.

中文翻译：

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SecureFace：人脸模板保护


事实证明，面部图像可以根据其表示（模板）进行重建。我们提出了一个随机 CNN，在给定输入人脸图像和用户特定密钥的情况下生成受保护的人脸生物识别模板。用户特定密钥的使用为安全模板引入了随机性，从而增强了模板的安全性。为了进一步增强模板的安全性，我们不存储密钥，而是存储一个安全草图，可以通过向系统提交的真实查询来解码该草图以生成密钥。我们使用三个人脸基准数据集（FRGC v2.0、CFP 和 IJB-A）评估了所提出的受保护模板生成方法。实验结果证明，该方法生成的受保护模板是不可逆且可取消的，同时保留了验证性能。