Learning based biometric key generation method using CNN and RNN

Studies on the biometric authentication and biometric key generation have been underway for a long time. Fundamentally, there is a disadvantage that it is difficult to obtain uniform biometrics due to noise, and issues of security and privacy are still mentioned, but high user convenience is an advantage that cannot be ignored. Recently, the results of existing researches on biometric key generation show very good results. However, since the algorithms presented in many studies are suited to the specific dataset, applying these algorithms to different datasets makes it difficult to achieve the good results mentioned in the paper. The reason is probably because most datasets are collected in one place with one camera. We wanted to present a key generation method that is not limited to datasets, and we came up with a training-based method for this. In this paper, we propose a method with the convolutional neural network (CNN) and the recurrent neural network (RNN) for cryptographic key generation from face biometrics. CNN is used to extract the feature vector from the face image, and RNN generates the key from the feature vector. In the registration process, the RNN is iteratively trained. Experimental results on the databases show that the proposed approach is effective in the biometric key generation. The results for mixed database also show good performance.