Individual identification is basis of animal behavior and ecology research, and is also important for the development of wildlife protection policies. In order to investigate new methods of giant panda (Ailuropoda melanoleuca) individual identification and population surveying, we photographed 18 giant pandas from Bifengxia giant panda base in Ya'an and the Chinese Giant Panda Garden in Gengda, Wenchuan County, Sichuan province in July 2017. We took high-quality facial photos of each giant panda 6 ~ 13 times for a total of 131 photographs. Using a developmental Network, we formulated a model for the facial recognition of the giant panda. We used the model to test photos which had some background features, with a successful individual recognition rate of 79.41%. We also tested photos in which the background was completely removed, which resulted in an individual recognition rate of 58.82%. This study shows that the developmental Network has adequate discriminatory ability for giant panda individual identification, and that the proportion of background features has severe influence on the actual results of the model. With the progress of the developmental network recognition model, we suggest that more wildlife researchers pursue this application of novel technology to conduct individual identification research of rare wildlife species such as the giant panda, gradually improve identification accuracies, and apply the model to large-scale wildlife investigations in key regions.