Automatic Identification of Individual Primates with Deep Learning Techniques

iScience

Pengfei Xu¹  Yewen Sun²  Colin A. Chapman³  Zhihui Shi⁴  Guofan Shao⁵  Gang He⁶  Dingyi Fang⁷  Xiaojiang Chen⁸  Qiguang Miao⁹  Baoguo Li⁹  He Zhang^1,10  Songtao Guo^1,10 

[1] Corresponding author;Department of Anthropology, Center for the Advanced Study of Human Paleobiology, George Washington University, Washington, DC 20037, USA;Institute of Internet of Things, Northwest University, Xi'an, China;School of Life Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South Africa;Shaanxi International Joint Research Centre for the Battery-free Internet of Things, Xi'an, China;Xi'an Key Laboratory of Big Data and Intelligent Vision, Xi'an 710071, China;Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USA;School of Computer Science and Technology, Xidian University, Xi'an 710071, China;School of Information Sciences and Technology, Northwest University, Xi'an 710127, China;Shaanxi Key Laboratory for Animal Conservation, School of Life Sciences, Northwest University, Xi'an 710069, China;

关键词: Zoology;    Ethology;    Artificial Intelligence;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: The difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.

【 授权许可】

Unknown