Clapham Melanie, Miller Ed, Nguyen Mary, Darimont Chris T
BearID Project Sooke BC Canada.
Department of Geography University of Victoria Victoria BC Canada.
Ecol Evol. 2020 Nov 6;10(23):12883-12892. doi: 10.1002/ece3.6840. eCollection 2020 Dec.
Emerging technologies support a new era of applied wildlife research, generating data on scales from individuals to populations. Computer vision methods can process large datasets generated through image-based techniques by automating the detection and identification of species and individuals. With the exception of primates, however, there are no objective visual methods of individual identification for species that lack unique and consistent body markings. We apply deep learning approaches of facial recognition using object detection, landmark detection, a similarity comparison network, and an support vector machine-based classifier to identify individuals in a representative species, the brown bear . Our open-source application, , detects a bear's face in an image, rotates and extracts the face, creates an "embedding" for the face, and uses the embedding to classify the individual. We trained and tested the application using labeled images of 132 known individuals collected from British Columbia, Canada, and Alaska, USA. Based on 4,674 images, with an 80/20% split for training and testing, respectively, we achieved a facial detection (ability to find a face) average precision of 0.98 and an individual classification (ability to identify the individual) accuracy of 83.9%. and its annotated source code provide a replicable methodology for applying deep learning methods of facial recognition applicable to many other species that lack distinguishing markings. Further analyses of performance should focus on the influence of certain parameters on recognition accuracy, such as age and body size. Combining with camera trapping could facilitate fine-scale behavioral research such as individual spatiotemporal activity patterns, and a cost-effective method of population monitoring through mark-recapture studies, with implications for species and landscape conservation and management. Applications to practical conservation include identifying problem individuals in human-wildlife conflicts, and evaluating the intrapopulation variation in efficacy of conservation strategies, such as wildlife crossings.
新兴技术助力应用野生动物研究进入新时代，生成从个体到种群层面的数据。计算机视觉方法可通过自动化物种和个体的检测与识别，处理基于图像技术生成的大型数据集。然而，除灵长类动物外，对于缺乏独特且一致身体标记的物种，尚无客观的个体视觉识别方法。我们应用基于目标检测、地标检测、相似性比较网络和支持向量机的分类器的深度学习面部识别方法，来识别代表性物种棕熊的个体。我们的开源应用程序在图像中检测熊的面部，旋转并提取面部，为面部创建一个“嵌入”，并使用该嵌入对个体进行分类。我们使用从加拿大不列颠哥伦比亚省和美国阿拉斯加收集的132个已知个体的标记图像对该应用程序进行训练和测试。基于4674张图像，分别以80/20%的比例划分训练集和测试集，我们实现了面部检测（找到面部的能力）平均精度为0.98，个体分类（识别个体的能力）准确率为83.9%。该应用程序及其带注释的源代码提供了一种可复制的方法，用于应用深度学习面部识别方法，适用于许多其他缺乏明显标记的物种。性能的进一步分析应关注某些参数对识别准确性的影响，如年龄和体型。将该应用程序与相机陷阱相结合，可促进精细尺度的行为研究，如个体时空活动模式，以及通过标记重捕研究进行种群监测的经济有效方法，对物种和景观保护与管理具有重要意义。在实际保护中的应用包括识别野生动物与人类冲突中的问题个体，以及评估保护策略（如野生动物通道）在种群内的效果差异。
