Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms

TL;DR

This paper presents a semi-supervised, non-invasive artificial vision-based method for recognizing the endangered Diploglossus millepunctatus lizard species, achieving high accuracy in segmentation and identification.

Contribution

It introduces an automatic segmentation and identification algorithm specifically designed for Diploglossus millepunctatus, reducing subjectivity and analysis time compared to traditional methods.

Findings

82.87% average correct segmentation

92.99% top-1 identification accuracy

96.82% top-5 identification accuracy

Abstract

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.