Hangxing Zang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Hangxing Zang has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Han Su
Sichuan Normal University
1 shared publication Mengnan He
Chengdu Research Base of Giant Panda Breeding Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu China
1 shared publication- Peng Chen
Chengdu Research Base of Giant Panda Breeding, The Conservation of Endangered Wildlife Key Laboratory of Sichuan Province, Chengdu, China
1 shared publication Peng Liu
Chengdu Research Base of Giant Panda Breeding Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu China
1 shared publicationPing Xu
Giant Panda National Park Chengdu Administration Chengdu China
1 shared publicationRong Hou
Chengdu Research Base of Giant Panda Breeding Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu China
1 shared publication
Latest Publications
- Open Access
- 4 December 2022
- Published by Wiley in Ecology and Evolution
- Vol. 12 (12) , e9507
Giant panda age recognition based on a facial image deep learning system
The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.
- 4 December 2022
- Published by Wiley in Ecology and Evolution
- Vol. 12 (12) , e9507
Open AccessGiant panda age recognition based on a facial image deep learning system
The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.