Deep Aging Face Verification With Large Gaps
Along with the long-time evolution of popular social networks, e.g. Facebook, social media analysis research inevitably arrived at the era of considering face/user recognition with large age gaps. However, related research with adequate subjects and large age gaps is surprisingly rare. In this work, we first collect a so-called cross-age face (CAFE) dataset, ranging from child, to young, to adult, to old groups. Then, we propose a novel framework, called deep aging face verification (DAFV), for this challenging task. DAFV includes two modules: aging pattern synthesis and aging face verification. The aging pattern synthesis module synthesizes the faces of all age groups for the input face of an arbitrary age, and the core structure is a deep aging-aware denoising auto-encoder ( a2-DAE) with multiple outputs. The aging face verification module then takes the synthesized aging patterns of a face pair as the input, and each pair of synthesized images of the same age group is fed into a parallel CNN; finally, all parallel CNN outputs are fused to provide similar/dissimilar prediction. For DAFV, the training of the aging face verification module easily suffers from the overfitting results from the aging pattern synthesis module, and we propose to use the cross- validation strategy to produce error-aware outputs for the synthesis module. Extensive experiments on the CAFE dataset well demonstrate the superiority of the proposed DAFV framework over other solutions for aging face verification.