Automatic Identification of Individual Primates with Deep Learning Techniques
Automatic Identification of Individual Primates with Deep Learning Techniques
复制标题
利用深度学习技术自动识别灵长类动物个体
DOI：
10.1016/j.isci.2020.101412
复制
发表时间：
2020-08-21
期刊：
影响因子：
5.8
通讯作者：
Li, Baoguo
中科院分区：
文献类型：
--
作者：
Guo, Songtao;Xu, Pengfei;Li, Baoguo
获得可靠的动物个体识别的困难限制了研究人员获得定量数据以解决重要的生态，行为和保护问题的能力。传统的标记方法将动物置于不适当的风险之中。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，需要一种工具来识别物种和个体。在这里，我们介绍了一个专门为自动人脸检测和个体识别开发的系统，该系统使用深度学习方法，使用视频和静止帧图像，可以可靠地用于多个物种。该系统使用一个数据集进行了训练和测试，该数据集包含41种灵长类物种的1，040个个体的102，399张图像，这些个体的个体身份已知，以及4种食肉动物物种的91个个体的6，562张图像。对于灵长类动物，该系统在94.1%的时间内正确识别个体，每秒可以处理31张面部图像。
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.