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Global standards for cattle recognition, registration and traceability are being developed. However missed or swapped cattle, false insurance claims and reallocation of cattle at slaughter houses are global problems throughout the world. Previous cattle recognition approaches have their own boundaries and they are not able to provide required level of security to cattle livestock. In this paper, an attempt has been made to minimize the above mentioned problems by descriptors automatic face recognition of cattle. The proposed multi-resolution algorithm extracts feature through Speeded Up Robust Feature (SURF) and Local Binary Patterns (LBP) from different Gaussian pyramid levels. The feature descriptors obtained at every Gaussian level area unit combined using weighted sum rule fusion techniques. The proposed algorithm yields rank-1 identification accuracy of 92.5 % on a cattle face database of 1200 cattle face image (120 subjects × 10 face image of each subject). Thus, in this paper, we have tried to demonstrate that identification of cattle based on their cattle face can be used to recognize the cattle and negate the notion that all cattle look alike.
Kumar et al. (Tue,) studied this question.