Giant panda age recognition based on a facial image deep learning system

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.

Beyond animal detection: a benchmark for detecting animal age group

“…They used a five-fold subjectexclusive (SE) protocol 33,34 to train seven Convolutional Neural Networks (CNN) based on ordinal regression, which ensured the same subject did not appear in the training set and testing set at the same time. Based on facial images of giant pandas, Qi et al 35 developed a deep learning method to explore age group classification and reached an accuracy of 85.99% on EfficientNet. Mazurek et al 15 developed an innovative approach to predicting a dog's age group using a photograph of its face.…”