Guo Songtao, Xu Pengfei, Miao Qiguang, Shao Guofan, Chapman Colin A, Chen Xiaojiang, He Gang, Fang Dingyi, Zhang He, Sun Yewen, Shi Zhihui, Li Baoguo
Shaanxi Key Laboratory for Animal Conservation, School of Life Sciences, Northwest University, Xi'an 710069, China.
School of Information Sciences and Technology, Northwest University, Xi'an 710127, China; Shaanxi International Joint Research Centre for the Battery-free Internet of Things, Xi'an, China; Institute of Internet of Things, Northwest University, Xi'an, China.
iScience. 2020 Aug 21;23(8):101412. doi: 10.1016/j.isci.2020.101412. Epub 2020 Jul 25.
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.
获取动物可靠个体识别的困难限制了研究人员获取定量数据以解决重要生态、行为和保护问题的能力。传统标记方法使动物面临不必要的风险。通过分析动物图像来识别物种的机器学习方法已被证明是成功的。但对于许多问题而言，需要一种不仅能识别物种，还能识别个体的工具。在此，我们介绍一种专门开发的系统，该系统使用深度学习方法，通过视频和静态图像进行自动面部检测和个体识别，可可靠地用于多个物种。该系统使用一个数据集进行训练和测试，该数据集包含41种灵长类动物中1040个个体的102399张图像（其个体身份已知）以及4种食肉动物中91个个体的6562张图像。对于灵长类动物，该系统在94.1%的时间内能够正确识别个体，并且每秒可处理31张面部图像。
