Reidentifying Asian Elephants from Ear Images Using a Cascade of Convolutional Neural Networks and Explaining with GradCAM

Zoologists visually recognise each Asian elephant ( Elephas maximus ), mainly based on their ear patterns. Towards automating this process, the existing methods on African elephants are less instrumental for Asian elephants, because the nick patterns are rare. This paper presents a cascade of Convolutional Neural Networks for uniquely detecting Asian elephants with two steps: (1) an elephant-ear localisation step at a species level, and (2) an ear-patch classification step at an individual level. First, a YOLO CNN with pre-trained weights on ImageNet is retrained with manually cropped elephant ears to localise them in the colour image. Second, these cropped ear patches are learnt by a CNN to classify each elephant by the Zoologist’s labelling; Xception outperformed VGG16, ResNet50, InceptionV3, and AlexNet in this second step on 56 elephants. Xception produced a top-1 accuracy of 88% and top-5 accuracy of 99.27% for reidentification as the best performance. Discriminative regions of elephant ears were visually explained by GradCAM on Xception reidentification classifier.