Automatic Cattle Identification based on Multi-Channel LBP on Muzzle Images

Every individual is unique. Biometrics authentication is mainly used to distinguish and identify each person. Parts of human body which are widely used for identification are fingerprints, faces and iris. In recent years, biometrics authentication begins to be used on animals, for the sake of population control, legal ownership and trade, and disease surveillance. This paper focuses on the automatic identification of cattles. Similar to human's fingerprint in term of uniqueness, cattle's muzzle is used in the identification process. In the conventional way, plastic ear tags are used to identify individual cattles. However, they can be worn down or lost easily. In addition, microchips are also used and implanted into cattles. This could injure them or cause some sickness. It is also expensive and requires human experts for the implant process. This paper introduces a novel solution using biometric images for the cattle identification. The proposed method extracts features from muzzle images using histogram of multi-channel Local Binary Pattern (LBP). This feature extraction is processed on sub-images to preserve the local spatial information of the muzzle patterns. Then, Support Vector Machine (SVM) is employed as the main classifier. The proposed method is evaluated using the published dataset containing 31 different cattles. It achieves the perfect performance of 100% accuracy.