As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

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In this paper, a palmprint augmentation algorithm based on 3D animation is proposed for enhancing contactless palmprint recognition performance. Contactless palmprint varies in position, orientation and musculoskeletal deformations. As the existing contactless databases are small, they contain only a few such variations of a palm. Popular data augmentation approaches, including translation, rotation and scaling, have been used to increase the dataset size and its diversity, but these methods do not simulate non-linear deformation of the hand. Some researchers have used 3D and computer graphic techniques to generate more data for training deep networks. These techniques are application-specific. The proposed algorithm makes use of a 3D hand model to simulate muscular and skeletal deformations of the hand. The deformations from the 3D model are applied to 2D palmprint images to generate new palmprint images with the same identities. Four deep networks, Alexnet, VGG-16, Resnet-50 and Inception-V3 and two contactless palmprint databases, IITD and CA-SIA, are employed to evaluate the proposed algorithm. The proposed algorithm is compared with the standard augmentation methods. The experimental results show that the proposed augmentation algorithm reduces EER and Rank-1 error rate.

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