Modeling of Facial Aging and Kinship: A Survey
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 2.
4 papers.
- 2021Canonical Saliency Maps: Decoding Deep Face Modelscited by 2
- 2025A Race Bias Free Face Aging Model for Reliable Kinship Verificationcited by 0
- 2026VLAgeBench: Benchmarking Large Vision-Language Models for Zero-Shot Human Age Estimationcited by 0
- 2026ReAge3D: Re-Aging 3D Faces with View Consistencycited by 0
- 2026ReAge3D: Re-Aging 3D Faces with View Consistency16 Jun 2026
- 2026VLAgeBench: Benchmarking Large Vision-Language Models for Zero-Shot Human Age Estimation27 Mar 2026
- 2025A Race Bias Free Face Aging Model for Reliable Kinship Verification18 Sep 2025
- 2021Canonical Saliency Maps: Decoding Deep Face Models4 May 2021