Abstract
Giant panda (panda) is a highly endangered animal. Significant efforts and resources have been put on panda conservation. To measure effectiveness of conservation schemes, estimating its population size in wild is an important task. The current population estimation approaches, including capture-recapture, human visual identification and collection of DNA from hair or feces, are invasive, subjective, costly or even dangerous to the workers who perform these tasks in wild. Cameras have been widely installed in the regions where pandas live. It opens a new possibility for non-invasive image based panda recognition. Panda face recognition is naturally a small dataset problem, because of the number of pandas in the world and the number of qualified images captured by the cameras in each encounter. In this paper, a panda face recognition algorithm, which includes alignment, large feature set extraction and matching is proposed and evaluated on a dataset consisting of 163 images. The experimental results are encouraging.
Abstract (translated)
大熊猫是一种高度濒危动物。大熊猫保护工作投入了大量的精力和资源。为了衡量保护计划的有效性，估计野生动物的种群规模是一项重要的任务。目前的人口估计方法，包括捕获、重新捕获、人类视觉识别和从头发或粪便中收集DNA，对在野外执行这些任务的工人来说是具有侵入性的、主观的、昂贵的，甚至是危险的。在大熊猫生活的地区，摄像机已被广泛安装。为无创图像熊猫识别开辟了新的可能性。熊猫人脸识别自然是一个小数据集问题，因为世界上熊猫的数量和每次遭遇中相机捕捉到的合格图像的数量。本文提出了一种基于163幅图像的熊猫人脸识别算法，该算法包括对齐、大特征集提取和匹配。实验结果令人鼓舞。
URL
https://arxiv.org/abs/1905.11163
