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2DPCA fractal features and genetic algorithm for efficient face representation and recognition

机译:2DPCA分形特征与高效面部表示与识别的遗传算法

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摘要

In this article, we present an automatic face recognition system. We show that fractal features obtained from Iterated Function System allow a successful face recognition and outperform the classical approaches. We propose a new fractal feature extraction algorithm based on genetic algorithms to speed up the feature extraction step. In order to capture the more important information that is contained in a face with a few fractal features, we use a bi-dimensional principal component analysis. We have shown with experimental results using two databases as to how the optimal recognition ratio and the recognition time make our system an effective tool for automatic face recognition.
机译:在本文中,我们提供了一种自动面部识别系统。我们表明,从迭代功能系统获得的分形功能允许成功的人脸识别和优于经典方法。我们提出了一种基于遗传算法的新分形特征提取算法,加快特征提取步骤。为了捕获具有少数分形功能的面部中包含的更重要的信息,我们使用双维主成分分析。我们已经使用了使用两个数据库的实验结果,以及最佳识别率和识别时间如何使我们的系统成为自动面部识别的有效工具。

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