Face Template Protection through Residual Learning Based Error-Correcting Codes

The leakage of the face template leads to severe security problems since the facial image is unique and irreplaceable to each individual. Many researchers have been devoted to protecting the face template. Nevertheless, to achieve high security for the face template, partial matching accuracy is usually sacrificed. The main challenge of this problem is the low inter-user variations and high intra-user variations of facial images. In this work, we propose a method integrating residual learning and error-correcting codes for face template protection. In particular, the proposed method consists of two major components: (a) a deep residual network component mapping facial images to polar codewords assigned to users, and (b) a polar decoder reducing noise brought by high intra-user variations in the predicted codewords. The proposed method is evaluated on extended Yale B, CMU-PIE, and FEI databases. It provides high security of face template and achieves a high (100%) genuine accept rate at a low false accept rate (0%) simultaneously, which outperforms most state-of-the-arts.