Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
This work addresses face age progression for applications like forensics or entertainment, but it is incremental as it adds occupational factors to existing methods.
The paper tackles the problem of face age progression by introducing an occupational face aging dataset and proposing an occupational-aware adversarial network that learns aging processes under different occupations while preserving personal characteristics. Experimental results demonstrate advantages over state-of-the-art methods.
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.