A recognition system is very useful to recognize human, object, and animals. An animal recognition system plays an important role in livestock biometrics, that helps in recognition and verification of livestock in case of missed or swapped animals, false insurance claims, and reallocation of animals at slaughter houses. In this research, we propose a fast and cost-effective animal biometrics based cattle recognition system to quickly recognize and verify the false insurance claims of cattle using their primary muzzle point image pattern characteristics. To solve this major problem, users (owner, parentage, or other) have captured the images of cattle using their smart devices. The captured images are transferred to the server of the cattle recognition system using a wireless network or internet technology. The system performs pre-processing on the muzzle point image of cattle to remove and filter the noise, increases the quality, and enhance the contrast. The muzzle point features are extracted and supervised machine learning based multi-classifier pattern recognition techniques are applied for recognizing the cattle. The server has a database of cattle images which are provided by the owners. Finally, One-Shot-Similarity (OSS) matching and distance metric learning based techniques with ensemble of classifiers technique are used for matching the query muzzle image with the stored database.A prototype is also developed for evaluating the efficacy of the proposed system in term of recognition accuracy and end-to-end delay.