INTRODUCTION

APPLICATIONS

DATASETS
3.1 Datasets with age labels
3.2 Datasets with kinship labels

AGE ESTIMATION
4.1 Geometric facial representation-based methods
4.2 Appearance-based methods
4.3 Subspace learning-based methods
4.4 Deep learning-based methods
4.5 Other representations
4.6 Evaluation protocols, metrics, and results for age estimation
4.7 Discussion on age estimation
4.8 Challenges in the wild

AGE PROGRESSION
5.1 Subspace Learning based Methods
5.2 Sequence modeling
5.3 3-Dimensional Representations
5.4 Deep learning-based representations
5.5 Evaluation protocols, metrics, and results for age progression
5.6 Discussion on age progression methods
5.7 Challenges in the wild

AGE-INVARIANT FACIAL CHARACTERIZATION
6.1 Age-invariant descriptors
6.2 Age-invariant subspace learning-based methods
6.3 Deep learning-based methods
6.4 Evaluation protocols, metrics, and results for age-invariant facial characterization
6.5 Discussion on age-invariant characterization methods
6.6 Challenges in the wild

KINSHIP VERIFICATION
7.1 Invariant descriptors to genetic variations
7.2 Subspace learning-based methods
7.3 Metric learning-based methods
7.4 Deep learning-based representations
7.5 Other representations
7.6 Evaluation protocols, metrics, and results for kinship verification
7.7 Discussion on kinship verification
7.8 Challenges in the wild

AGING AND KINSHIP

CONCLUSIONS