  Age progression is defined as aesthetically re-rendering the aging face at
any future age for an individual face. In this work, we aim to automatically
render aging faces in a personalized way. Basically, for each age group, we
learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles),
where the dictionary bases corresponding to the same index yet from two
neighboring aging dictionaries form a particular aging pattern cross these two
age groups, and a linear combination of all 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 person 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 person,
yet much easier and more practical to get face pairs from neighboring age
groups. To this end, we propose a novel Bi-level Dictionary Learning based
Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary
learning is formulated to learn the aging dictionaries based on face pairs from
neighboring age groups. Extensive experiments well demonstrate the advantages
of the proposed BDL-PAP over other state-of-the-arts in term of personalized
age progression, as well as the performance gain for cross-age face
verification by synthesizing aging faces.
