A Robust Feature-processing Method for Age-invariant Face Recognition

In this paper, we present a novel feature-processing method for age-invariant face recognition so that it is robust to aging process. The main purpose of feature-processing is to remove aging effects while keep personalized properties stable simultaneously. In order to achieve this, we try to learn a space map and then encode the mapped feature to an age - invariant representation. In the encoding step, we introduce two kinds of constraints: the temporal constraint (local constraint) and boundary constraint (global constraint). We applied our feature-processing method to Cross-Age Celebrity Dataset (CACD). In order to verify the versatility of our method, we apply it to both high-dimensional LBP feature and deep feature. Results show that our feature-processing method works well on CACD and the face verification subset of CACD (CACD-VS).