Modeling of Facial Aging and Kinship: A Survey

TL;DR

This survey reviews recent advances in computational facial aging and kinship modeling, covering datasets, representations, evaluation protocols, and future challenges for real-world applications.

Contribution

It provides a comprehensive overview of current methods, datasets, and evaluation metrics for facial aging and kinship modeling, highlighting research gaps and future directions.

Findings

Compiled an extensive list of annotated datasets.

Analyzed geometric, handcrafted, and learned facial representations.

Reviewed evaluation protocols and notable experimental results.

Abstract

Computational facial models that capture properties of facial cues related to aging and kinship increasingly attract the attention of the research community, enabling the development of reliable methods for age progression, age estimation, age-invariant facial characterization, and kinship verification from visual data. In this paper, we review recent advances in modeling of facial aging and kinship. In particular, we provide an up-to date, complete list of available annotated datasets and an in-depth analysis of geometric, hand-crafted, and learned facial representations that are used for facial aging and kinship characterization. Moreover, evaluation protocols and metrics are reviewed and notable experimental results for each surveyed task are analyzed. This survey allows us to identify challenges and discuss future research directions for the development of robust facial models in real-world conditions.