# Kinship Identification through Joint Learning Using Kinship Verification Ensembles

arXiv: https://arxiv.org/abs/2004.06382 · HTML: https://mlsanity.com/paper/2004.06382/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Meta-Mining Discriminative Samples for Kinship Verification](https://mlsanity.com/paper/2103.15108/) (2021)
- [Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical](https://mlsanity.com/paper/2109.02219/) (2021)
- [ChildPredictor: A Child Face Prediction Framework with Disentangled Learning](https://mlsanity.com/paper/2204.09962/) (2022)
- [Kinship Verification through a Forest Neural Network](https://mlsanity.com/paper/2504.18910/) (2025)

## Abstract (arXiv)

Kinship verification is a well-explored task: identifying whether or not two persons are kin. In contrast, kinship identification has been largely ignored so far. Kinship identification aims to further identify the particular type of kinship. An extension to kinship verification run short to properly obtain identification, because existing verification networks are individually trained on specific kinships and do not consider the context between different kinship types. Also, existing kinship verification datasets have biased positive-negative distributions which are different than real-world distributions. To this end, we propose a novel kinship identification approach based on joint training of kinship verification ensembles and classification modules. We propose to rebalance the training dataset to become more realistic. Large scale experiments demonstrate the appealing performance on kinship identification. The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.