Automatic individual recognition of Japanese macaques (Macaca fuscata) from sequential images

Automatic individual recognition techniques can support data collection in the field of ethology. Recent studies have contributed to development of automatic individual recognition techniques using machine learning and deep learning. However, varied conditions in the wild, such as the presence of oc...

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Bibliographic Details
Published in:Ethology 2022-05, Vol.128 (5), p.461-470
Main Authors: Ueno, Masataka, Kabata, Ryosuke, Hayashi, Hidetaka, Terada, Kazunori, Yamada, Kazunori
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Bayesian analysis
Classifiers
Data collection
Deep learning
Ethology
Head movement
Image filters
individual recognition
Japanese macaques
Macaca fuscata
Machine learning
Mathematical models
Neural networks
Object recognition
primates
sequential Bayesian filter
Statistical inference
Video data
wild
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Summary:Automatic individual recognition techniques can support data collection in the field of ethology. Recent studies have contributed to development of automatic individual recognition techniques using machine learning and deep learning. However, varied conditions in the wild, such as the presence of occlusions and head rotations of individuals, can lower the accuracy of automatic recognition techniques. Thus, there is requirement for improvement in the accuracy and robustness of these techniques. In this study, we have used previously observed information updated with given current observation by Bayesian inference to improve the automatic individual recognition of free‐ranging Japanese macaques (Macaca fuscata) at Katsuyama, Japan. We collected static images and video footage of 51 adult individuals. Using the static images, we created eight individual recognition systems (classifiers), using GoogLeNet and ResNet‐18 as convolutional neural network models. Additionally, sequential data of the faces of the macaques were obtained from 86 video recordings of the 51 individuals to evaluate the classifiers. We were able to successfully recognize 90% or more individuals with each classifier through the combination of the sequential Bayesian filter and the classifier. Eighty‐five percent or more of the individuals had posterior probabilities of 90% or above when conducting recognition tests using the sequential Bayesian filter with 10 images. The best classifier recognized 98% of individuals using 10 images and all individuals using 50 images. Recognition was also successful when the sequential Bayesian filter was applied to cases in which the recognition rate was
ISSN:0179-1613
1439-0310
DOI:10.1111/eth.13277