Deep Face Image Retrieval for Cancelable Biometric Authentication

Young Kyun Jang, Nam Ik Cho

Abstract: This paper presents a cancelable biometric system for face authentication by exploiting the convolutional neural network (CNN)-based face image retrieval system. For the cancelable biometrics we must build a template that achieves good performance while maintaining some essential conditions. First the same template should not be used in different applications. Second if the compromise event occurs original biometric data should not be retrieved from the template. Last the template should be easily discarded and recreated. Hence we propose a Deep Table-based Hashing (DTH) framework that encodes CNN-based features into a binary code by utilizing the index of the hashing table. We employ noise embedding and intra-normalization that distorts biometric data which enhances the non-invertibility. For training we propose a new segment-clustering loss and pairwise Hamming loss with two classification losses. The final authentication results are obtained by voting on the outcome of the retrieval system. Experiments conducted on two large scale face image datasets demonstrate that the proposed method works as a proper cancelable biometric system.