Giant panda face recognition based on PandaFaceNet

Individual recognition of animals via infrared camera trapping surveys is an important method for protecting and monitoring animals in the wild. However, several factors limit current survey methods used for individual animal recognition, such as the lack of accuracy and extensive time required to process data. Recently, new technologies and methods for individual recognition of animal images have been developed for rare wildlife species (e.g., giant pandas and lemurs). These new technologies require adequate and high-quality sampled images; however, it can be challenging for researchers to obtain an adequate sample size of wildlife images from the field. To overcome this problem, we proposed and tested a new small-sample individual recognition method adapted from FaceNet called PandaFaceNet, using data from a self-built giant panda (Ailuropoda melanoleuca) facial image database. We tested the proposed giant panda individual recognition method on unknown captive and wild giant panda datasets. The results showed that this method has 95.3% recognition accuracy for distinguishing among two captive giant panda facial images and 91% recognition accuracy for distinguishing among two wild giant pandas. Notably, PandaFaceNet achieves individual recognition through comparing two images and is an open-set identification method. Therefore, PandaFaceNet provides a novel method for giant panda research by opening up opportunities for analysis of small sample sizes of panda imagery data, while also providing new directions for research on rare wildlife more broadly.