Personalized Age Progression with Aging Dictionary
Personalized Age Progression with Aging Dictionary
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DOI：
10.1109/iccv.2015.452
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发表时间：
2015-10
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影响因子：
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通讯作者：
Xiangbo Shu;Jinhui Tang;Hanjiang Lai;Luoqi Liu;Shuicheng Yan
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文献类型：
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作者：
Xiangbo Shu;Jinhui Tang;Hanjiang Lai;Luoqi Liu;Shuicheng Yan
在本文中，我们的目标是自动绘制老化的人脸在个性化的方式。基本上，学习一组特定于年龄组的字典，其中对应于相同索引但来自不同字典的字典库形成跨不同年龄组的特定老化过程模式，并且这些模式的线性组合表示特定的个性化老化过程。此外，在词典学习过程中考虑了两个因素。首先，除了老化字典之外，每个受试者可能具有额外的个性化面部特征，例如痣，其在老化过程中是不变的。其次，为特定主题收集所有年龄组的面孔是具有挑战性的，甚至是不可能的，但从相邻年龄组获得面孔对要容易得多，也更实用。因此，个性意识的耦合重建损失被用来学习字典的基础上，从相邻的年龄组的脸对。大量的实验很好地证明了我们提出的解决方案在个性化老化进展方面优于其他最先进的解决方案，以及通过合成老化面孔来进行跨年龄人脸验证的性能增益。
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.