Cattle Face Recognition Using Local Binary Pattern Descriptor

In response to the current need for positive identification of cattle traceability, this paper presents a novel facial representation model of cattle based on local binary pattern (LBP) texture features and some extended LBP descriptors are also introduced. Algorithm training was performed independently on several normalized gray face images of 30 cattle (with each having a set of six, seven, eight, and nine images respectively). Robust alignment by sparse and low-rank decomposition was also used to align the images because of variations in illumination, image misalignment and occlusion in the test image. The performance of this technique was assessed on a separate set of images using the weighted Chi square distance [1]. The LBP descriptor shows its excellence in efficiency and accuracy with regard to the encouraging results on cattle face recognition. More training sets and modified algorithms will be considered to improve recognition rates. Future work should aim at improving the automation of the system and combining the LBP histogram with other effective histograms.