  Non-intrusive biometrics of animals using images allows to analyze phenotypic
populations and individuals with patterns like stripes and spots without
affecting the studied subjects. However, non-intrusive biometrics demand a well
trained subject or the development of computer vision algorithms that ease the
identification task. In this work, an analysis of classic segmentation
approaches that require a supervised tuning of their parameters such as
threshold, adaptive threshold, histogram equalization, and saturation
correction is presented. In contrast, a general unsupervised algorithm using
Markov Random Fields (MRF) for segmentation of spots patterns is proposed.
Active contours are used to boost results using MRF output as seeds. As study
subject the Diploglossus millepunctatus lizard is used. The proposed method
achieved a maximum efficiency of $91.11\%$.
