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We put forward a video dataset with 5k+ facial bounding box annotationsacross a troop of 7 western lowland gorillas at Bristol Zoo Gardens. Trainingon this dataset, we implement and evaluate a standard deep learning pipeline onthe task of facially recognising individual gorillas in a zoo environment. Weshow that a basic YOLOv3-powered application is able to perform identificationsat 92% mAP when utilising single frames only. Tracking-by-detection-associationand identity voting across short tracklets yields an improved robustperformance of 97% mAP. To facilitate easy utilisation for enriching theresearch capabilities of zoo environments, we publish the code, video dataset,weights, and ground-truth annotations at data.bris.ac.uk.

A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments