Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research offered new methodological perspectives through automatization of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identifications done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching a 82.2% accuracy and (ii) the creation of an individual recognizer for K{\=o}jima island macaques population (YOLOv8n model), reaching a 83% accuracy. We also created a K{\=o}jima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this innovative approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.

翻译：个体识别在生态学和行为学中扮演着关键角色，尤其是作为理解复杂社会结构的工具。然而，传统识别方法常涉及侵入性物理标记，既可能干扰动物行为，又需耗费研究人员大量时间。近年来，深度学习与研究的融合通过自动化复杂任务提供了新的方法论视角。研究人员越来越多地利用目标检测与识别技术，从视频素材中实现个体识别。本研究初步探索了利用深度学习开发非侵入性工具，用于日本猕猴（Macaca fuscata）的面部检测与个体识别。该研究的最终目标是，基于数据集中的识别结果，自动生成所研究种群的社会网络表征。当前主要成果令人鼓舞：（i）构建了日本猕猴面部检测器（Faster-RCNN模型），准确率达82.2%；（ii）开发了幸岛猕猴种群的个体识别器（YOLOv8n模型），准确率达83%。我们还通过传统方法，基于视频中的共现关系构建了幸岛种群的社会网络，为自动生成网络的可靠性评估提供了基准。这些初步成果证明了这一创新方法的潜力，有望为科学界提供追踪个体及研究日本猕猴社会网络的工具。