What is it about?
Animal biometrics based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features, and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This study presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust features and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of the Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of 93.87% identification accuracy. The comparative analysis of experimental results for proposed work and appearance-based face recognition algorithms has been done at each level.
Why is it important?
The research highlights important key points: 1- The identification and monitoring of animals in the livestock or farmhouse are major problems due to failures of traditional animal identification methodologies such as ear-taggings, ear-tattoos, freeze-brandings, and hot-iron based marking and invasive approaches 2- These approaches are invasive approaches. They can easily duplicate and forged. 3- The false insurance claims and registration of animals are the major problems. In this paper, we proposed a working prototype system for recognition of individual cattle based on their biometric features such as muzzle point image pattern.
The following have contributed to this page: Mr Santosh Kumar and Dr. Sanjay Singh