Papers › Automated Detection of Cat Facial Landmarks

Automated Detection of Cat Facial Landmarks

15 Oct 2023arXiv:2310.09793archive 2025-07-28

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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Tasks

AnatomyFacial Landmark Detection

Datasets

Introduced by this paper, per the archive.

CatFLW

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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.

Methods

Convolution

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