Panthera leo (the African lion) is now an endangered species. Over the last 20 years approximately 42% of its habitat has been lost, creating fragmented populations across the African continent. The accurate monitoring of lion populations and a better understanding of the connectivity between them is critical to maintaining the genetic viability of these increasingly isolated populations.
The Lion Identification Network of Collaborators (LINC) is an open-source platform designed to change the base methodologies of how lion research can be enacted within these fragmented geographies and diverse conservation efforts. LINC employs a custom web application, innovative AI tools and a collaborative database allowing the consolidation and retrieval of lion data by conservationists, researchers and government wildlife management. LINC also provides a platform for social interaction and data sharing between conservation efforts and government institutions, shaping and informing conservation policy. The LINC project has built this foundation with ten partner organizations, KWS (Kenyan Wildlife Service) and support from Microsoft and the National Geographic Society. This strong interlinked research community enables conservationists and decision-makers to pinpoint priority areas nationally and internationally.
The workflow of the LINC system is as follows: conservationist capture in field images of individual lions which they transfer into the community database. Once the images are entered in the system, identification is performed using a set of ML techniques. The first technique uses facial features, while the other employs a whisker spot matching method. The predictions are returned to the conservationists via the UI who then correlates the appropriate metadata and informs on the ground teams of the lion movements.
This talk will focus on the development of the Machine Learning (ML) models for identifying individual lions across varied image sets, reducing the time and human resource needed to utilize large data collections. While human face identification has been an active (and sometimes controversial) field of research, the case of Computer Vision (CV) for unique individual identification for different animal species has largely not been tackled by the research community.
First, we will go over the challenges of the unique data set of african lions, and cover how deep learning techniques are used to identify unique individuals across time using facial features. Later, we will talk about the process of whisker spot pattern matching – a technique widely applied in field by conservationists since the 1960’s, and still the dominant method today. We will dive into several techniques for automating the pattern matching, and speak about some promising results. Lastly, we will suggest research directions for the future, that could also be of use for identification of other species.