  Face aging is of great importance for cross-age recognition and
entertainment-related applications. Recently, conditional generative
adversarial networks (cGANs) have achieved impressive results for face aging.
Existing cGANs-based methods usually require a pixel-wise loss to keep the
identity and background consistent. However, minimizing the pixel-wise loss
between the input and synthesized images likely resulting in a ghosted or
blurry face. To address this deficiency, this paper introduces an Attention
Conditional GANs (AcGANs) approach for face aging, which utilizes attention
mechanism to only alert the regions relevant to face aging. In doing so, the
synthesized face can well preserve the background information and personal
identity without using the pixel-wise loss, and the ghost artifacts and
blurriness can be significantly reduced. Based on the benchmarked dataset
Morph, both qualitative and quantitative experiment results demonstrate
superior performance over existing algorithms in terms of image quality,
personal identity, and age accuracy.
