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Automatic Cattle Identification based on Muzzle Photo Using Speed-Up Robust Features Approach

机译：使用加速鲁棒特征方法基于枪口照片自动识别牛

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摘要

Cattle identification has been a serious problem for breeding association. The need of a robust identification method is a must. The previous identification means have not been satisfactory. The biometric marking has been investigated to be a permanent marking of the individual. Muzzle pattern or nose print has the same characteristic with the human fingerprint which is the most popular biometric marker. SURF approach which is an object recognition based method has been evaluated for the automatic cattle identification purpose. Based on the experiment result SURF approach outperforms the previous research that is used eigenface algorithm. The original SURF approach relatively can handle non-normalized data set (scale and orientation invariant) with high accuracy and precision. With a sufficient training data, the performance of the original SURF can be more than 0.9 in accuracy and kappa statistic. The U-SURF as another version of the original SURF has shown an outstanding performance more than the original SURF and eigenface algorithm, but only in the orientation normalized data.
机译：牛的鉴定一直是育种协会的一个严重问题。必须有一个可靠的识别方法。先前的识别手段并不令人满意。生物特征标记已被研究为个人的永久标记。枪口图案或鼻印与人类指纹具有相同的特征，后者是最流行的生物识别标记。 SURF方法是一种基于对象识别的方法，已被评估用于牛的自动识别。基于实验结果，SURF方法优于以前使用特征脸算法的研究。原始的SURF方法相对可以高精度和高精度地处理非标准化数据集（比例尺和方向不变）。有了足够的训练数据，原始SURF的性能在准确性和kappa统计上可以超过0.9。作为原始SURF的另一个版本，U-SURF比原始SURF和本征面算法表现出更出色的性能，但是仅在方向归一化数据中。

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