  Face is one of the predominant means of person recognition. In the process of
ageing, human face is prone to many factors such as time, attributes, weather
and other subject specific variations. The impact of these factors were not
well studied in the literature of face aging. In this paper, we propose a novel
holistic model in this regard viz., ``Face Age progression With Attribute
Manipulation (FAWAM)", i.e. generating face images at different ages while
simultaneously varying attributes and other subject specific characteristics.
We address the task in a bottom-up manner, as two submodules i.e. face age
progression and face attribute manipulation. For face aging, we use an
attribute-conscious face aging model with a pyramidal generative adversarial
network that can model age-specific facial changes while maintaining intrinsic
subject specific characteristics. For facial attribute manipulation, the age
processed facial image is manipulated with desired attributes while preserving
other details unchanged, leveraging an attribute generative adversarial network
architecture. We conduct extensive analysis in standard large scale datasets
and our model achieves significant performance both quantitatively and
qualitatively.
