# Personalized Age Progression with Bi-level Aging Dictionary Learning

## TL;DR

This paper introduces a novel bi-level dictionary learning method for personalized age progression, effectively modeling aging patterns and individual facial features to generate realistic aging faces and improve cross-age face verification.

## Contribution

It proposes a bi-level dictionary learning framework that captures personalized aging patterns and invariant facial features from face pairs of neighboring age groups.

## Key findings

- Outperforms state-of-the-art methods in personalized age progression
- Enhances cross-age face verification accuracy
- Successfully models individual aging characteristics

## Abstract

Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way. Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all 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 person 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 person, yet much easier and more practical to get face pairs from neighboring age groups. To this end, we propose a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary learning is formulated to learn the aging dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of the proposed BDL-PAP over other state-of-the-arts in term of personalized age progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.