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Sci Adv. 2019 Sep 4;5(9):eaaw0736. doi: 10.1126/sciadv.aaw0736. eCollection 2019 Sep.

Chimpanzee face recognition from videos in the wild using deep learning.

Author information

Primate Models for Behavioural Evolution Lab, Institute of Cognitive and Evolutionary Anthropology, University of Oxford, Oxford, UK.
Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, UK.
Primate Research Institute, Kyoto University, Inuyama, Japan.
Department of Zoology, University of Oxford, Oxford, UK.
Gorongosa National Park, Sofala, Mozambique.
Interdisciplinary Center for Archaeology and Evolution of Human Behaviour (ICArEHB), Universidade do Algarve, Faro, Portugal.
Centre for Functional Ecology-Science for People & the Planet, Universidade de Coimbra, Coimbra, Portugal.


Video recording is now ubiquitous in the study of animal behavior, but its analysis on a large scale is prohibited by the time and resources needed to manually process large volumes of data. We present a deep convolutional neural network (CNN) approach that provides a fully automated pipeline for face detection, tracking, and recognition of wild chimpanzees from long-term video records. In a 14-year dataset yielding 10 million face images from 23 individuals over 50 hours of footage, we obtained an overall accuracy of 92.5% for identity recognition and 96.2% for sex recognition. Using the identified faces, we generated co-occurrence matrices to trace changes in the social network structure of an aging population. The tools we developed enable easy processing and annotation of video datasets, including those from other species. Such automated analysis unveils the future potential of large-scale longitudinal video archives to address fundamental questions in behavior and conservation.

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