Unique lion identification using triplet loss and siamese networks

Abstract

Former techniques for the identification of lion individuals (Panthera leo) relied on manual methods of recording data. Such processes have various shortcomings due to the manual nature of recording this data. This research work aims to automate the process of encoding the uniqueness within the whisker spot patterns for each lion individual by non-invasively using photographs. Towards this research work the main bottleneck was the availability of image data for individual lions. The proposed model embeds the uniqueness within the patterns for a specific individual as a unique cluster within its embedding space. This is achieved by using a triplet loss function which, due to its one-shot learning nature trains a deep inception network with less training data. Photographic images are known to have variations in lighting, pose variation, angle variation and other inconsistencies. Since the nature of these issues are nonlinear, it is preferred to create the target model using deep learning techniques. An inception network is trained to generate 128-dimensional vectors unique to each lion. This research paper elaborates on such deep machine learning techniques and other processes that are used to create this model.