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Efficient face recognition method using RBF kernel and genetic algorithm

机译:基于RBF核和遗传算法的高效人脸识别方法

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There area wider range of biometric authentication systems available; Face recognition system is one of them. It is an effective way for authentication and also considers many security aspects. Any face recognition system has to handle higher amount and dimension of image data. Whenever we consider this system at global level a large variety of problems frequently arrives. To overcome these problems this paper introduces a new model. This newly designed system uses radial basis function (RBF) kernel for management of small training sets of high dimension images with genetic algorithm based weight optimization technique. This system is effective for large datasets thus it uses genetic algorithm. Genetic algorithm provides fast learning and trains RBF neural network effectively, it reduces searching efforts and significantly reduces the time taken for recognition. This system is very effective at public places due to huge rush of public and provides fast and accurate recognition.
机译:可以使用的生物识别系统范围更广;人脸识别系统就是其中之一。这是进行身份验证的有效方法,并且考虑了许多安全方面。任何人脸识别系统都必须处理更大数量和更大尺寸的图像数据。每当我们在全球范围内考虑该系统时,就会经常遇到各种各样的问题。为了克服这些问题,本文介绍了一种新模型。该新设计的系统使用径向基函数(RBF)内核,通过基于遗传算法的权重优化技术来管理小型高维图像训练集。该系统对大型数据集有效,因此使用遗传算法。遗传算法可提供快速学习并有效地训练RBF神经网络,从而减少了搜索工作并显着减少了识别时间。由于公众的忙碌,该系统在公共场所非常有效,并提供快速准确的识别。

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