Robust feature encoding for age-invariant face recognition

Abstract

Large age range is a serious obstacle for automatic face recognition. Although many promising results have been reported, it still remains a challenging problem due to significant intra-class variations caused by the aging process. In this paper, we mainly focus on finding an expressive age-invariant feature such that it is robust to intra-personal variance and discriminative to different subjects. To achieve this goal, we map the original feature to a new space in which the feature is robust to noise and large intra-personal variations caused by aging face images. Then we further encode the mapped feature into an age-invariant representation. After mapping and encoding, we get the robust and discriminative feature for the specific purpose of age-invariant face recognition. To show the effectiveness and generalizability of our method, we conduct experiments on two well-known public domain databases for age-invariant face recognition: Cross-Age Celebrity Dataset (CACD, the largest publicly available cross-age face dataset) and MORPH dataset. Experiments show that our method achieves state-of-the-art results on these two challenging datasets.