Potency of individual identification of Japanese macaques (Macaca fuscata) using a face recognition system and a limited number of learning images
Potency of individual identification of Japanese macaques (Macaca fuscata) using a face recognition system and a limited number of learning images
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DOI：
10.3106/ms2020-0071
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发表时间：
2021-01-01
期刊：
影响因子：
1
通讯作者：
Ogawa, Hitoshi
中科院分区：
文献类型：
--
作者：
Otani, Yosuke;Ogawa, Hitoshi
个体识别是动物研究中的一项重要技术，需要研究人员的培训和专业技能。使用人工智能（AI）深度学习的人脸识别系统已投入实际使用，用于识别人类和动物，但系统构建需要大量带注释的学习图像。在野生动物研究案例中，很难准备大量的学习图像，这可能是为什么使用AI的系统尚未广泛应用于实地研究的原因。为了显示使用少量学习图像识别个体的系统的发展潜力，我们构建了一个系统，从平均每个个体20个图像的少量候选个体中识别单个日本猕猴（Macaca fuscata yakui）。该系统的特点是增加数据，同时确定四个单独的识别模型和识别的大多数五帧，以确保可靠性。这项技术对各种利益相关者具有高度的实用性，预计它将推动可广泛用于实地研究的人工智能个人识别系统的开发。
Individual identification is an important technique in animal research that requires researcher training and specialized skillsets. Face recognition systems using artificial intelligence (AI) deep learning have been put into practical use to identify in humans and animals, but a large number of annotated learning images are required for system construction. In wildlife research cases, it is difficult to prepare a large amount of learning images, which may be why systems using AI have not been widely used in field research. To show the potential for the development of a system that identifies individuals using a small number of learning images, we constructed a system to identify individual Japanese macaques (Macaca fuscata yakui) from a small number of candidate individuals from an average of 20 images per individual. The characteristics of this system were augmentation of data, simultaneous determination by four individual identification models and identification from a majority of five frames to ensure reliability. This technology has a high degree of utility for various stakeholders and it is expected that it will advance the development of individual identification systems by AI that can be widely used in field research.