Giant panda age recognition based on a facial image deep learning system. Issue 12 (4th December 2022)

• Record Type: Journal Article
• Authors: Qi, Yu; Su, Han; Hou, Rong; Zang, Hangxing; Liu, Peng; He, Mengnan; Xu, Ping; Zhang, Zhihe; Chen, Peng
• Is Part Of: Ecology and evolution. Volume 12:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-04
• Subjects: age classification -- convolutional neural network -- deep learning -- giant panda -- wildlife ecology; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.9507
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: 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. Abstract : In this work, a deep learning method was developed to study the distinctiveness of panda faces for age group classification. The experimental results show that the face of the giant panda contains some age information, and it is feasible to identify the approximate age group of the giant panda through the analysis of facial images.