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Title: Chimpanzee face recognition from videos in the wild using deep learning
Authors: Schofield, Daniel
Nagrani, Arsha
Zisserman, Andrew
Hayashi, Misato
Matsuzawa, Tetsuro
Biro, Dora
Carvalho, Susana
Author's alias: 林, 美里
松沢, 哲郎
Issue Date: 4-Sep-2019
Publisher: American Association for the Advancement of Science (AAAS)
Journal title: Science Advances
Volume: 5
Issue: 9
Thesis number: eaaw0736
Abstract: 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.
Description: 深層学習による野生のビデオ記録からのチンパンジーの顔認識に成功. 京都大学プレスリリース. 2019-09-05.
Rights: © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
URI: http://hdl.handle.net/2433/243879
DOI(Published Version): 10.1126/sciadv.aaw0736
Related Link: http://www.kyoto-u.ac.jp/ja/research/research_results/2019/190905_1.html
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