Face Age Progression with Attribute Manipulation

Sinzith Tatikonda, Athira M. Nambiar, Anurag Mittal

Abstract: The human face is one of the predominant means of person recognition. Human faces are affected by many factors i.e. time, attributes, weather, and other subject-specific variations. Although face aging has been studied in the past, the impact of the aforesaid factors, especially, the effect of attributes on the aging process were unexplored. In this paper, we propose a novel holistic “Face Age progression With Attribute Manipulation” (FAWAM) model that generates face images at different ages while simultaneously varying attributes and other subject specific characteristics. We address the task in a bottom-up manner, considering both age and attributes submodules. 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. Our proposed model achieves significant qualitative as well as quantitative performance results.