# CatFLW: Cat Facial Landmarks in the Wild Dataset

arXiv: https://arxiv.org/abs/2305.04232 · HTML: https://mlsanity.com/paper/2305.04232/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 7 May 2023.

## Builds on (most cited first, 8 of 8)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [Survey on Emotional Body Gesture Recognition](https://mlsanity.com/paper/1801.07481/) (2018)
- [AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces](https://mlsanity.com/paper/1909.04951/) (2019)
- [Face Recognition: Primates in the Wild](https://mlsanity.com/paper/1804.08790/) (2018)
- [Towards Automatic Identification of Elephants in the Wild](https://mlsanity.com/paper/1812.04418/) (2018)
- [Human and Sheep Facial Landmarks Localisation by Triplet Interpolated Features](https://mlsanity.com/paper/1509.04954/) (2015)

## Built on by (most cited first, 5 of 5)

- [Open-Vocabulary Animal Keypoint Detection with Semantic-feature Matching](https://mlsanity.com/paper/2310.05056/) (2023)
- [CattleFace-RGBT: RGB-T Cattle Facial Landmark Benchmark](https://mlsanity.com/paper/2406.03431/) (2024)
- [DogFLW: Dog Facial Landmarks in the Wild Dataset](https://mlsanity.com/paper/2405.11501/) (2024)
- [Matching Semantically Similar Non-Identical Objects](https://mlsanity.com/paper/2403.08227/) (2024)
- [Semantic Style Transfer for Enhancing Animal Facial Landmark Detection](https://mlsanity.com/paper/2505.05640/) (2025)

## Abstract (arXiv)

Animal affective computing is a quickly growing field of research, where only recently first efforts to go beyond animal tracking into recognizing their internal states, such as pain and emotions, have emerged. In most mammals, facial expressions are an important channel for communicating information about these states. However, unlike the human domain, there is an acute lack of datasets that make automation of facial analysis of animals feasible. This paper aims to fill this gap by presenting a dataset called Cat Facial Landmarks in the Wild (CatFLW) which contains 2016 images of cat faces in different environments and conditions, annotated with 48 facial landmarks specifically chosen for their relationship with underlying musculature, and relevance to cat-specific facial Action Units (CatFACS). To the best of our knowledge, this dataset has the largest amount of cat facial landmarks available. In addition, we describe a semi-supervised (human-in-the-loop) method of annotating images with landmarks, used for creating this dataset, which significantly reduces the annotation time and could be used for creating similar datasets for other animals. The dataset is available on request.