Personalized Age Progression with Aging Dictionary
This work addresses the problem of generating realistic, personalized aging faces for applications like face verification, though it is incremental in its approach.
The paper tackles personalized age progression of faces by learning age-group specific dictionaries that model aging patterns, while accounting for invariant personal characteristics and using face pairs from neighboring age groups for training. It demonstrates advantages over state-of-the-art methods in personalized aging and improves cross-age face verification through synthesized aging faces.
In this paper, we aim to automatically render aging faces in a personalized way. Basically, a set of age-group specific dictionaries are learned, where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups, and a linear combination of these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each subject may have extra personalized facial characteristics, e.g. mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups. Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.