Papers › Automated Detection of Cat Facial Landmarks
Automated Detection of Cat Facial Landmarks
George Martvel, Ilan Shimshoni, Anna Zamansky
The field of animal affective computing is rapidly emerging, and analysis of facial expressions is a crucial aspect. One of the most significant challenges that researchers in the field currently face is the scarcity of high-quality, comprehensive datasets that allow the development of models for facial expressions analysis. One of the possible approaches is the utilisation of facial landmarks, which has been shown for humans and animals. In this paper we present a novel dataset of cat facial images annotated with bounding boxes and 48 facial landmarks grounded in cat facial anatomy. We also introduce a landmark detection convolution neural network-based model which uses a magnifying ensembe method. Our model shows excellent performance on cat faces and is generalizable to human facial landmark detection.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Facial Landmark Detection | CatFLW | ELD (EfficientNetV2S) | NME | 2.83 | #1 of 3 | Archive leaderboard | report |
| Facial Landmark Detection | CatFLW | ELD (EfficientNetV2B0) | NME | 2.98 | #2 of 3 | Archive leaderboard | report |
| Facial Landmark Detection | CatFLW | ELD (MobileNetV2) | NME | 3.09 | #3 of 3 | Archive leaderboard | report |
| Facial Landmark Detection | WFLW | ELD (EfficientNetV2B1) | NME | 4.65 | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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