A study on giant panda recognition based on images of a large proportion of captive pandas

Abstract

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.

Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears

“…Deep learning techniques automatically detect and extract learned features from data, and provide a powerful alternative to traditional methods of feature extraction (see Christin et al, 2019 andSchneider et al, 2019 for ecological applications). Face recognition using deep learning has recently achieved an accuracy of up to 92.5% for chimpanzees Pan troglodytes (Schofield et al, 2019) and 96.3% for giant pandas Ailuropoda melanoleuca (Chen et al, 2020); the latter possessing distinctive eye patch markings that could aid identification. A primary challenge, however, is that deep learning requires large labeled datasets for training and testing, which are difficult to acquire for wild populations, especially at the individual level (Schneider et al, 2019).…”

Revisiting animal photo-identification using deep metric learning and network analysis

“…unknown individuals. However, despite the availability of proven and efficient techniques [13] and several successful attempts to apply the method to non-human species [14,15,16,17,18,19,20,21,22,23], re-identification remains a challenging task when applied to animals in wild population where re-observations are limited sensu largo [24].…”

“…In other words, this sampling design implies to solve the re-identification in a mixture of known and unknown individuals. Chen and colleagues [23] referred to this problem as the "open set" identification problem, and they proposed to identify images from unknown individuals and to assign them a single "unknown" label. Here, we moved one step further and evaluated the possibility to build a model capable of identifying all individuals, be they known or unknown.…”

“…unknown individuals. However, despite the availability of proven and efficient techniques (Zheng et al ., 2016), and several successful attempts to apply the method to non-human species (Körschens et al ., 2018; Hansen et al ., 2018; Moskvyak et al ., 2019; Bouma et al ., 2019; Schofield et al ., 2019; He et al ., 2019; Bogucki et al ., 2019; Schneider et al ., 2020; Chen et al ., 2020; Ferreira et al ., 2020), re-identification remains a challenging task when applied to animals in the wild where re-observations are limited in number to train the model satisfactorily sensu largo (Schneider et al ., 2019).…”

“…In other words, this standard sampling design implies to solve the re-identification in a mixture of known and unknown individuals. Chen et al . (2020) referred to this problem as the “open set” identification problem, and they proposed to identify images from unknown individuals and to assign them a single “unknown” label.…”