Disentangled representation learning and residual GAN for age-invariant face verification

Abstract

One of the challenges in cross-age face recognition and verification is to effectively model the facial aging process. Despite the rapid advances in face-related areas, it is still very difficult for existing methods to simultaneously achieve accurate facial feature preservation and reliable aging during aging modeling. Aiming to address this issue, we introduce a Disentangled Representation learning and Residual Generative Adversarial Network (DR-RGAN) that represents the facial features without age interference, which is achieved by explicitly disentangling the facial features and age variation. An encoder-decoder structured generator produces aging images from unstructured facial representations by using the age characteristics provided separately. It can thus take the disentangled representation to preserve personal identity for face verification. Considering that pixel-based errors may cause a loss of detail, a VGG based content loss is further equipped to preferably preserve facial features. The discriminator is trained to distinguish the real from generated faces, carry out identification prediction, and leverage an age estimator to boost the aging accuracy. It is beneficial for obtaining more photorealistic and desirable aging effects, as well as more consistent face verification results. Extensive experiments on the CACD and LFW datasets demonstrate that our DR-RGAN generates pleasing aging imageries and achieves a high accuracy of face verification.