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DogFLW: Dog Facial Landmarks in the Wild Dataset

The Dog Facial Landmarks in the Wild (DogFLW) dataset offers 3,274 images of dogs' faces in various environments and conditions, each annotated with 46 facial landmarks and a bounding box on the dogs' face. This dataset is inspired by the CatFLW (Cat Facial Landmarks in the Wild set) and designed to aid researchers in analyzing dog emotions and behaviors.

The development of the landmark scheme was carried out by experts certified in DogFACS coding.  It enables detailed facial expressions, guided by the anatomy of dogs' face. The result is a detailed set of 46 points capturing essential facial features, including the nose, eyes, mouth, and ears.

THE LANDMARKS

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brow left

ear left upper intersection with head

ear left upper third

lip l corner

lip r upper mid

ear right upper intersection with head

lip r corner

ear r lower intersection with head

eye r outer

lip lower middle

chin

ear left lower intersection with head

tongue tip

brow right

eye r upper

ear left lower third

eye r lower

eye r inner

zygomus right

zygomus left

ear left tip

ear right tip

nose bottom

nostrils r outer

ear r upper 2thirds

nose upper

ear r upper third

ear r lower third

ear r lower 2thirds

whiskerspad r mid

snout right

eye l upper

eye l outer

eye l lower

eye l inner

snout left

nosepad mid

nose r upperedge

nostrils middle

nose l upperedge

nostrils r outer

lip l upper mid

lip upper middle

whiskerspad l mid

ear r lower 2thirds

ear r upper 2thirds

The dataset draws from the Stanford Dog dataset and includes individual dogs from 120 different breeds. The images are varied in resolution, and are set in non-laboratory conditions, capturing real-world environments.

To evaluate the dataset, we used the Ensemble Landmark Detector (ELD) model, which provided benchmark results using normalized mean error (NME) as a performance metric. The results show high accuracy in detecting landmarks around the eyes and nose but present challenges with ears due to their variability in shape and position across breeds. Despite these challenges, the DogFLW dataset serves as a robust resource for understanding dog facial expressions. For detailed information, please see DogFLW: Dog Facial Landmarks in the Wild Dataset paper.

We believe that animal affective computing crucially depends on the availability of openly available annotated datasets. We are proud to lay the foundations for automated analysis of animal facial expressions, and we invite the scientific community to help us develop this direction.

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The dataset creation was generously supported by the Data Science Research Center (DSRC) of the University of Haifa.

Project Team

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PhD Candidate

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​Chiara Canori

Advisor

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Data Management Lead

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Dr. Greta Abele

Advisor

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​Giulia Pedretti

Advisor

COLLABORATORS

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