# DogFLW: Dog Facial Landmarks in the Wild Dataset

arXiv: https://arxiv.org/abs/2405.11501 · HTML: https://mlsanity.com/paper/2405.11501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 19 May 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild](https://mlsanity.com/paper/2003.03771/) (2020)
- [Structured Landmark Detection via Topology-Adapting Deep Graph Learning](https://mlsanity.com/paper/2004.08190/) (2020)
- [AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces](https://mlsanity.com/paper/1909.04951/) (2019)
- [PFLD: A Practical Facial Landmark Detector](https://mlsanity.com/paper/1902.10859/) (2019)
- [Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources](https://mlsanity.com/paper/1703.00862/) (2017)
- [Structured Feature Learning for Pose Estimation](https://mlsanity.com/paper/1603.09065/) (2016)
- [Interspecies Knowledge Transfer for Facial Keypoint Detection](https://mlsanity.com/paper/1704.04023/) (2017)
- [AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses](https://mlsanity.com/paper/2007.03221/) (2020)
- [Human and Sheep Facial Landmarks Localisation by Triplet Interpolated Features](https://mlsanity.com/paper/1509.04954/) (2015)
- [CatFLW: Cat Facial Landmarks in the Wild Dataset](https://mlsanity.com/paper/2305.04232/) (2023)
- [Automated Detection of Cat Facial Landmarks](https://mlsanity.com/paper/2310.09793/) (2023)
- [A Two-Step Learning Method For Detecting Landmarks on Faces From Different Domains](https://mlsanity.com/paper/1809.04621/) (2018)

## Built on by (most cited first, 1 of 1)

- [Semantic Style Transfer for Enhancing Animal Facial Landmark Detection](https://mlsanity.com/paper/2505.05640/) (2025)

## Abstract (arXiv)

Affective computing for animals is a rapidly expanding research area that is going deeper than automated movement tracking to address animal internal states, like pain and emotions. Facial expressions can serve to communicate information about these states in mammals. However, unlike human-related studies, there is a significant shortage of datasets that would enable the automated analysis of animal facial expressions. Inspired by the recently introduced Cat Facial Landmarks in the Wild dataset, presenting cat faces annotated with 48 facial anatomy-based landmarks, in this paper, we develop an analogous dataset containing 3,274 annotated images of dogs. Our dataset is based on a scheme of 46 facial anatomy-based landmarks. The DogFLW dataset is available from the corresponding author upon a reasonable request.