Cross-age face verification by coordinating with cross-face age verification

In this paper we present a novel framework for cross-age face verification (FV) by seeking help from its “competitor” named cross-face age verification (AV), i.e., deciding whether two face photos are taken at similar ages. While FV and AV share some common features, FV pursues age insensitivity and AV seeks age sensitivity. Such correlation suggests that AV may be used to guide feature selection in FV, i.e., by reducing the chance of choosing age sensitive features. Driven by this intuition, we propose to learn a solution for cross-age face verification by coordinating with a solution for age verification. Specifically, a joint additive model is devised to simultaneously handling both tasks, while encoding feature coordination by a competition regularization term. Then, an alternating greedy coordinate descent (AGCD) algorithm is developed to solve this joint model. As shown in our experiments, the algorithm effectively balances feature sharing and feature exclusion between the two tasks; and, for face verification, the algorithm effectively removes distracting features used in age verification. To evaluate the proposed algorithm, we conduct cross-age face verification experiments using two benchmark cross-age face datasets, FG-Net and MORPH. In all experiments, our algorithm achieves very promising results and outperforms all previously tested solutions.