Formal Accuracy Analysis Of A Biometric Data Transformation And Its Application To Secure Template Generation

Many of the known secure template constructions transform real-valued feature vectors to integer-valued vectors, and then apply cryptographic transformations. Throughout this two-step transformation, the original biometric data is distorted, whence it is natural to expect some loss in the accuracy. As a result, the accuracy and security of the whole system should be analyzed carefully. In this paper, we provide a formal accuracy analysis of a generic and intuitive method to transform real-valued feature vectors to integer-valued vectors. We carefully parametrize the transformation, and prove some accuracy-preserving properties of the transformation. Second, we modify a recently proposed noise-tolerant template protection algorithm and combine it with our transformation. As a result, we obtain a secure biometric authentication method that works with real-valued feature vectors. A key feature of our scheme is that a second factor (e.g., user password, or public/private key) is not required, and therefore, it offers certain advantages over cancelable biometrics or homomorphic encryption methods. Finally, we verify our theoretical findings through implementations over public face and keystroke dynamics datasets and provide some comparisons.