Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder

• URL: http://arxiv.org/abs/2507.10552v1
• Date: Mon, 14 Jul 2025 17:59:59 GMT
• Title: Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder
• Authors: Vladimir Iashin, Horace Lee, Dan Schofield, Andrew Zisserman,
• Abstract summary: This study introduces a fully self-supervised approach to learning robust chimpanzee face embeddings from unlabeled camera-trap footage.<n>We train Vision Transformers on automatically mined face crops, eliminating the need for identity labels.<n>This work underscores the potential of self-supervised learning in biodiversity monitoring and paves the way for scalable, non-invasive population studies.
• Score: 48.03572115000886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Camera traps are revolutionising wildlife monitoring by capturing vast amounts of visual data; however, the manual identification of individual animals remains a significant bottleneck. This study introduces a fully self-supervised approach to learning robust chimpanzee face embeddings from unlabeled camera-trap footage. Leveraging the DINOv2 framework, we train Vision Transformers on automatically mined face crops, eliminating the need for identity labels. Our method demonstrates strong open-set re-identification performance, surpassing supervised baselines on challenging benchmarks such as Bossou, despite utilising no labelled data during training. This work underscores the potential of self-supervised learning in biodiversity monitoring and paves the way for scalable, non-invasive population studies.

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