Personalized Age Progression with Aging Dictionary

TL;DR

This paper introduces a personalized age progression method using aging dictionaries that model individual aging patterns and invariant facial features, improving face aging synthesis and verification accuracy.

Contribution

It proposes a novel aging dictionary learning approach that incorporates personalized facial traits and handles limited data by using face pairs from neighboring age groups.

Findings

Outperforms state-of-the-art methods in personalized aging synthesis

Enhances cross-age face verification accuracy

Demonstrates effectiveness through extensive experiments

Abstract

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.