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Chimpanzee face recognition from videos in the wild using deep learning

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TLDR
A deep convolutional neural network approach is presented that provides a fully automated pipeline for face detection, tracking, and recognition of wild chimpanzees from long-term video records, and generates co-occurrence matrices to trace changes in the social network structure of an aging population.
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.

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Journal ArticleDOI

A Review of Face Recognition Technology

TL;DR: Face recognition has become the future development direction and has many potential application prospects and is introduced in the general evaluation standards and the general databases of face recognition.
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Multi-animal pose estimation, identification and tracking with DeepLabCut

TL;DR: In this paper , a pose estimation toolbox for multi-animal tracking is presented, which integrates the ability to predict an animal's identity to assist tracking in case of occlusions.
Journal ArticleDOI

Deep learning-based methods for individual recognition in small birds

TL;DR: In this article, the authors describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds, including sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata.
Journal ArticleDOI

Perspectives in machine learning for wildlife conservation

TL;DR: In this article , the authors argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge, which could improve inputs for ecological models and lead to integrated hybrid modeling tools.
Book

Chimpanzee behavior in the wild : an audio-visual encyclopedia

利貞 西田
TL;DR: A large number of the studied sites are occupied by chimpanzees and bonobos, suggesting that thezee-Bonobos hybridisation is a natural process and not a cause for alarm.
References
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Proceedings ArticleDOI

Deep Residual Learning for Image Recognition

TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI

ImageNet: A large-scale hierarchical image database

TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
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