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.

该论文提出了一种自动化、个性化生成老化人脸的方法。利用一组特定年龄组的字典来学习老化过程，其中相同索引的字典基础表现为不同年龄组的老化特征，可线性组合生成个人的老化特征。在字典学习过程中，考虑每个受试者额外的个人化面部特征和采集领域的限制。该解决方案在个性化老化进程和跨年龄人脸验证合成老化人脸方面表现出优越的性能。

个性化年龄进展与老化字典