Perspectives on Individual Animal Identification from Biology and Computer Vision

Abstract

Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations, and propose how they might be addressed in the future.

Fig. 1

(a) Animal biometrics examples featuring unique distinguishable phenotypic traits (adapted with permission from unsplash.com). (b) Three pictures each of three example tigers from the Amur Tiger reID Dataset (Shuyuan et al. 2019) and three pictures each of three example bears from the McNeil River State Game Sanctuary (photo credit Alaska Department of Fish and Game). The tiger stripes are robust visual biometrics. The bear images highlight the variations across seasons (fur and weight changes). Postures and contexts vary more or less depending on the species and dataset and further complicate identification. (c) Machine learning identification pipeline from raw data acquisition through feature extraction to identity retrieval.