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首页> 外文期刊>Current Journal of Applied Science and Technology >Classification of Ear Biometric Data using SupportVector Machine
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Classification of Ear Biometric Data using SupportVector Machine

机译：支持向量机对耳朵生物特征数据的分类

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In this paper, a method to recognize persons using ear biometrics has been proposed. We propose a method to classify ears based on supervised learning using Support Vector Machine (SVM). For this, ear has been considered as a planar surface of irregular shape. The shape based features like distribution of area, moment of inertia (MI) with respect to minor and major axis and radius of gyration with respect to minor and major axis are considered.A database of 605 ears were considered in the development of the model. SVM was able to classify the ears into three groups. A recognition accuracy of 93% has been recorded. The clusters so formed were analyzed for precision, recall, f-measure and kappa statistics. The results showed that the SVM is a robust method.
机译：在本文中，提出了一种使用耳朵生物特征识别人的方法。我们提出一种使用支持​​向量机（SVM）的基于监督学习的耳朵分类方法。为此，耳朵被认为是不规则形状的平面。考虑基于形状的特征，例如面积分布，相对于短轴和长轴的惯性矩（MI）和相对于短轴和长轴的回转半径。在模型开发过程中考虑了605耳的数据库。 SVM能够将耳朵分为三类。记录的识别精度为93％。对如此形成的集群进行分析，以进行准确性，召回率，f测度和kappa统计。结果表明，SVM是一种可靠的方法。

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