  Face age progression, which aims to predict the future looks, is important
for various applications and has been received considerable attentions.
Existing methods and datasets are limited in exploring the effects of
occupations which may influence the personal appearances. In this paper, we
firstly introduce an occupational face aging dataset for studying the
influences of occupations on the appearances. It includes five occupations,
which enables the development of new algorithms for age progression and
facilitate future researches. Second, we propose a new occupational-aware
adversarial face aging network, which learns human aging process under
different occupations. Two factors are taken into consideration in our aging
process: personality-preserving and visually plausible texture change for
different occupations. We propose personalized network with personalized loss
in deep autoencoder network for keeping personalized facial characteristics,
and occupational-aware adversarial network with occupational-aware adversarial
loss for obtaining more realistic texture changes. Experimental results well
demonstrate the advantages of the proposed method by comparing with other
state-of-the-arts age progression methods.
