# Achieving Better Kinship Recognition Through Better Baseline

arXiv: https://arxiv.org/abs/2006.11739 · HTML: https://mlsanity.com/paper/2006.11739/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [15M Multimodal Facial Image-Text Dataset](https://mlsanity.com/paper/2407.08515/) (2024)
- [The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces](https://mlsanity.com/paper/2111.00598/) (2021)
- [Kinship Representation Learning with Face Componential Relation](https://mlsanity.com/paper/2304.04546/) (2023)
- [KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning](https://mlsanity.com/paper/2309.10641/) (2023)
- [Supervised Contrastive Learning and Feature Fusion for Improved Kinship Verification](https://mlsanity.com/paper/2302.09556/) (2023)
- [Facial Kinship Verification from remote photoplethysmography](https://mlsanity.com/paper/2309.08006/) (2023)

## Abstract (arXiv)

Recognizing blood relations using face images can be seen as an application of face recognition systems with additional restrictions. These restrictions proved to be difficult to deal with, however, recent advancements in face verification show that there is still much to gain using more data and novel ideas. As a result face recognition is a great source domain from which we can transfer the knowledge to get better performance in kinship recognition as a source domain. We present a new baseline for an automatic kinship recognition task and relatives search based on RetinaFace[1] for face registration and ArcFace[2] face verification model. With the approach described above as the foundation, we constructed a pipeline that achieved state-of-the-art performance on two tracks in the recent Recognizing Families In the Wild Data Challenge.