Publication
Chimpanzee face recognition from videos in the wild using deep learning
| dc.contributor.author | Schofield, Daniel | |
| dc.contributor.author | Nagrani, Arsha | |
| dc.contributor.author | Zisserman, Andrew | |
| dc.contributor.author | Hayashi, Misato | |
| dc.contributor.author | Matsuzawa, Tetsuro | |
| dc.contributor.author | Biro, Dora | |
| dc.contributor.author | Carvalho, Susana | |
| dc.date.accessioned | 2019-11-13T12:15:29Z | |
| dc.date.available | 2019-11-13T12:15:29Z | |
| dc.date.issued | 2019 | |
| dc.description.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. | pt_PT |
| dc.description.sponsorship | Agência financiadora Número do subsídio Engineering & Physical Sciences Research Council (EPSRC) EP/M013774/1 Cooperative Research Program of Primate Research Institute, Kyoto University Google Clarendon Fund Boise Trust Fund Wolfson College, University of Oxford Leverhulme Trust PLP-2016-114 Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science 16H06283 Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science LGP-U04 | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1126/sciadv.aaw0736 | |
| dc.identifier.issn | 2375-2548 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/12879 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | American Association for the Advancement of Science | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.title | Chimpanzee face recognition from videos in the wild using deep learning | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 9 | pt_PT |
| oaire.citation.startPage | eaaw0736 | pt_PT |
| oaire.citation.title | Science Advances | pt_PT |
| oaire.citation.volume | 5 | pt_PT |
| person.familyName | Carvalho | |
| person.givenName | Susana | |
| person.identifier.ciencia-id | C91A-A704-6E70 | |
| person.identifier.orcid | 0000-0003-4542-3720 | |
| person.identifier.scopus-author-id | 23977799600 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 1f6a7971-6b67-4f1a-9b1d-f18729d02e9e | |
| relation.isAuthorOfPublication.latestForDiscovery | 1f6a7971-6b67-4f1a-9b1d-f18729d02e9e |