# Interspecies Knowledge Transfer for Facial Keypoint Detection

arXiv: https://arxiv.org/abs/1704.04023 · HTML: https://mlsanity.com/paper/1704.04023/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2017.

## Built on by (most cited first, 13 of 13)

- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding](https://mlsanity.com/paper/2306.00576/) (2023)
- [A Novel Dataset for Keypoint Detection of quadruped Animals from Images](https://mlsanity.com/paper/2108.13958/) (2021)
- [Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks](https://mlsanity.com/paper/2111.07047/) (2021)
- [SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae](https://mlsanity.com/paper/2110.15082/) (2021)
- [Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation](https://mlsanity.com/paper/2108.13258/) (2021)
- [CattleFace-RGBT: RGB-T Cattle Facial Landmark Benchmark](https://mlsanity.com/paper/2406.03431/) (2024)
- [Towards Multi-Modal Animal Pose Estimation: A Survey and In-Depth Analysis](https://mlsanity.com/paper/2410.09312/) (2024)
- [One-shot domain adaptation for semantic face editing of real world images using StyleALAE](https://mlsanity.com/paper/2108.13876/) (2021)
- [Pose Recognition in the Wild: Animal pose estimation using Agglomerative Clustering and Contrastive Learning](https://mlsanity.com/paper/2111.08259/) (2021)
- [A lightweight 3D dense facial landmark estimation model from position map data](https://mlsanity.com/paper/2308.15170/) (2023)
- [DogFLW: Dog Facial Landmarks in the Wild Dataset](https://mlsanity.com/paper/2405.11501/) (2024)
- [ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer](https://mlsanity.com/paper/2510.11073/) (2025)

## Abstract (arXiv)

We present a method for localizing facial keypoints on animals by transferring knowledge gained from human faces. Instead of directly finetuning a network trained to detect keypoints on human faces to animal faces (which is sub-optimal since human and animal faces can look quite different), we propose to first adapt the animal images to the pre-trained human detection network by correcting for the differences in animal and human face shape. We first find the nearest human neighbors for each animal image using an unsupervised shape matching method. We use these matches to train a thin plate spline warping network to warp each animal face to look more human-like. The warping network is then jointly finetuned with a pre-trained human facial keypoint detection network using an animal dataset. We demonstrate state-of-the-art results on both horse and sheep facial keypoint detection, and significant improvement over simple finetuning, especially when training data is scarce. Additionally, we present a new dataset with 3717 images with horse face and facial keypoint annotations.