  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.
