Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms
This provides a tool for conservationists to monitor endangered lizards without invasive methods, though it is incremental as it applies existing vision techniques to a new species.
The paper tackled the problem of non-invasive biometric identification of the endangered Diploglossus millepunctatus lizard species using artificial vision algorithms, achieving 82.87% correct segmentation and 92.99% top-1 identification accuracy.
Animal biometrics is an important requirement for monitoring and conservation tasks. The classical animal biometrics risk the animals' integrity, are expensive for numerous animals, and depend on expert criterion. The non-invasive biometrics techniques offer alternatives to manage the aforementioned problems. In this paper we propose an automatic segmentation and identification algorithm based on artificial vision algorithms to recognize Diploglossus millepunctatus. Diploglossus millepunctatus is an endangered lizard species. The algorithm is based on two stages: automatic segmentation to remove the subjective evaluation, and one identification stage to reduce the analysis time. A 82.87% of correct segmentation in average is reached. Meanwhile the identification algorithm is achieved with euclidean distance point algorithms such as Iterative Closest Point and Procrustes Analysis. A performance of 92.99% on the top 1, and a 96.82% on the top 5 is reached. The developed software, and the database used in this paper are publicly available for download from the web page of the project.