Using muzzle pattern recognition as a biometric approach for cattle identification
Using muzzle pattern recognition as a biometric approach for cattle identification
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DOI：
10.13031/2013.23121
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发表时间：
2007-05-01
影响因子：
1.5
通讯作者：
Ward, S.
中科院分区：
文献类型：
--
作者：
Barry, B.;Gonzales-Barron, U. A.;Ward, S.
由于目前需要积极识别牛的可追溯性，这项工作的目的是调查的可行性，使用枪口图案作为一个基于生物特征的识别牛通过获取枪口图案，通过解除油墨印刷和通过数字图像。一个三阶段的匹配算法进行了评估，扫描枪口油墨印刷品，并成功地在所有情况下进行。数字成像的枪口是远远简单的油墨打印升降法。对于这些数字图像，使用主成分分析和欧氏距离分类器的技术。对来自29头牛的不同数量的标准化口吻图像独立进行算法训练（每组动物2、4、6、8和10张训练图像）。在单独的一组图像上评估该技术的性能（每只动物3个标准化的口吻图像）。结果表明，当使用230个特征向量（290），识别率为98.85%，和额外的特征向量没有提高识别率。正如预期的那样，较少的主成分（小于230）降低了识别率，而每只动物的训练图像的数量更高，提高了它。虽然结果已经证明了作为一种非侵入性的，廉价的，准确的生物特征识别的牛的枪口模式识别的潜力，进一步的研究走向自动化是必要的。
Arising from the current need for positive identification for cattle traceability, the objective of this work was to investigate the feasibility of using muzzle pattern as a biometric-based identifier for cattle by acquiring muzzle patterns through lifted ink prints and through digital images. A three-stage matching algorithm was evaluated for scanned muzzle ink prints and performed successfully in all cases. Digital imaging of muzzles was far simpler than the ink print lifting method. For these digital images, the techniques of principal component analysis and Euclidean distance classifier were used. The algorithm training was performed independently on a different number of normalized muzzle images from 29 cattle (sets of 2, 4, 6, 8, and 10 training images per animal). The performance of this technique was assessed on a separate set of images (3 normalized muzzle images per animal). Results showed that when using 230 eigenvectors (out of 290), the recognition rate was 98.85%, and that additional eigenvectors did not improve the recognition rate. As expected, fewer principal components (less than 230) reduced the recognition rate, while a higher number of training images per animal improved it. Although the results have demonstrated the potential of muzzle pattern recognition as a non-invasive, inexpensive, and accurate biometric identifier of cattle, further research towards automation is necessitated.