Hash-based iris template protection using maximum entropy binary codes and CNNs

As biometric authentication becomes more common, protecting biometric data is becoming increasingly important. One widely used protection method is encryption. However, not all encryption methods are suitable for biometric data. On the one hand, the encryption solution can lead to worse performance or on the other hand, not fulfill all required security measures. This paper proposes a solution for iris template protection that results in hash-encrypted data using Maximum Entropy Binary (MEB) codes and Convolutional Neural Networks (CNN). The method is compared to a baseline approach to demonstrate competitive recognition performance. In addition, the privacy and security properties of the template protection are evaluated.