Muzzle-Based Cattle Identification Using Speed up Robust Feature Approach
Muzzle-Based Cattle Identification Using Speed up Robust Feature Approach
复制标题
使用加速鲁棒特征方法进行基于枪口的牛识别
DOI：
10.1109/incos.2015.60
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
V. Snás̃el
中科院分区：
文献类型：
--
作者：
Shaimaa A. El;T. Gaber;Alaa Tharwat;A. Hassanien;V. Snás̃el
从上个世纪开始，动物识别对于几个目的变得重要，例如跟踪、控制牲畜交易和疾病控制。用于在农场或实验室中实现此类动物识别的侵入性和传统方法。为了避免这种侵入性并获得更准确的识别结果，出现了生物特征识别方法。本文提出了一种基于不变性生物特征的身份识别系统，根据牛的口纹图像对牛进行身份识别。该系统利用加速鲁棒特征（SURF）的特征提取技术，沿着与最小距离和支持向量机（SVM）分类器。所提出的系统的目标是使用最少数量的SURF兴趣点获得最佳精度，从而最大限度地减少系统完成准确识别所需的时间。它还比较了通过不同的分类器从SURF特征获得的准确性。实验运行217枪口印图像和实验结果表明，我们提出的方法取得了很好的识别率相比，以前的工作。
Starting from the last century, animals identification became important for several purposes, e.g. tracking, controlling livestock transaction, and illness control. Invasive and traditional ways used to achieve such animal identification in farms or laboratories. To avoid such invasiveness and to get more accurate identification results, biometric identification methods have appeared. This paper presents an invariant biometric-based identification system to identify cattle based on their muzzle print images. This system makes use of Speeded Up Robust Feature (SURF) features extraction technique along with with minimum distance and Support Vector Machine (SVM) classifiers. The proposed system targets to get best accuracy using minimum number of SURF interest points, which minimizes the time needed for the system to complete an accurate identification. It also compares between the accuracy gained from SURF features through different classifiers. The experiments run 217 muzzle print images and the experimental results showed that our proposed approach achieved an excellent identification rate compared with other previous works.