Automatic identification of wildlife using local binary patterns

Recognition of individuals is necessary for accurate estimation of wildlife population dynamics for effective management and conservation. Identifying individual wildlife by their distinctive body marks is one of the least invasive methods available. Although widely practiced, this method is mostly manual where newly captured images are compared with those in the library of previously captured images. The ability to do so automatically using computer vision techniques can improve speed and accuracy, facilitate on-field matching, and so on. This paper reports the results of using a texture based image feature descriptor, the Local Binary Patterns (LBP), for the automatic identification of an important endangered species - The Great Crested Newt (GCN). The proposed approach is tested on a database of newts' distinctive belly images which are treated as a source of biometric information. Results indicate that when both appearance and spatial information of newt belly patterns are encoded into a composite LBP feature vector, the discriminating power of the system can improve significantly.