Fine-Grained Giant Panda Identification

The image-based fine-grained identification of individual giant pandas (Ailuropoda melanoleuca) is an emerging technology, and it is extraordinarily challenging due to the extremely subtle visual differences between individual giant pandas and limited annotated training data. To address these challenges, we propose the Feature-Fusion Convolutional Neural Network with Patch Detector (FFCNN-PD) algorithm, which exploits the discriminative local patches and builds a hierarchical representation generated by fusing both global and local features. Specifically, an attentional cross-channel pooling is embedded in the FFCNN-PD to improve the class-specific patch detectors. In addition, we propose a new giant panda identification dataset (iPanda-30) to establish a benchmark. Experiments on the proposed iPanda-30 dataset and other fine-grained recognition datasets demonstrate the effectiveness of the FFCNN-PD algorithm against the existing state-of-the-arts.