Similar to human biometrics such as faces and fingerprints, animals also have biometrics for individual identifiers. This research paper works on biometrics of cattle using images of muzzle patterns. The proposed approach begins with a training process to construct a cattle face localization model using a Haar feature-based cascade classifier. Then, the watershed technique is applied to segment a region of interest (RoI) of a muzzle area in the detected region of the cattle face. This muzzle ROI is further enhanced to make ridge lines more outstanding. The next step, using two approaches, is to extract a main feature descriptor based on a bag of histograms of oriented gradients (BoHoG) and a histogram of local binary patterns (LBP). Then, the support vector machine (SVM) is applied with the histogram intersection kernel for a final cattle identifier. The proposed method is evaluated using five different datasets including one existing cattle dataset used in previous research works, one newly collected dataset of swamp buffalo captured in a controlled environment, and three newly collected datasets of swamp buffalo captured in an outdoor field environment. This outdoor field environment includes challenges of freely moving cattle and differences in daylight. It could achieve a promising accuracy of 95% for a large dataset of 431 subjects.

中文翻译：

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使用枪口图案识别牛的生物特征

与面部和指纹等人类生物特征相​​似，动物也具有用于个体标识符的生物特征。这篇研究论文使用枪口图案的图像研究牛的生物特征。所提出的方法从使用基于 Haar 特征的级联分类器构建牛脸定位模型的训练过程开始。然后，应用分水岭技术在检测到的牛脸区域中分割枪口区域的感兴趣区域（RoI）。此枪口 ROI 进一步增强，使脊线更加突出。下一步，使用两种方法，基于方向梯度直方图（BoHoG）和局部二值模式直方图（LBP）提取主要特征描述符。然后，支持向量机 (SVM) 与直方图交叉核一起应用于最终的牛标识符。所提出的方法使用五个不同的数据集进行评估，包括一个在以前的研究工作中使用的现有牛数据集，一个在受控环境中捕获的新收集的沼泽水牛数据集，以及在室外野外环境中捕获的三个新收集的沼泽水牛数据集。这种户外野外环境包括自由移动牛的挑战和日光差异。对于 431 名受试者的大型数据集，它可以实现 95% 的有希望的准确率。以及在户外野外环境中捕获的三个新收集的沼泽水牛数据集。这种户外野外环境包括自由移动牛的挑战和日光差异。对于 431 名受试者的大型数据集，它可以实现 95% 的有希望的准确率。以及在户外野外环境中捕获的三个新收集的沼泽水牛数据集。这种户外野外环境包括自由移动牛的挑战和日光差异。对于 431 名受试者的大型数据集，它可以实现 95% 的有希望的准确率。