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Gabor feature-based complete fisher discriminant framework for facial feature extraction

机译:基于Gabor特征的完整Fisher判别框架用于面部特征提取

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In this paper, we propose a novel feature extraction approach using Gabor feature based complete fisher discriminant algorithm (GCFD). Four main steps are involved in the proposed GCFD: (i) Gabor features of different scales and orientations are extracted by the convolution of Gabor filter bank and original gray images; (ii) Complete fisher discriminant algorithm (CFD) is used for feature dimensionality reduction and to extract all discrimination information; (iii) Feature fusion algorithm and Euclidean distance based nearest neighbor classifier are finally used for classification. (iv)Simulation results show the effectiveness of our proposed GCFD.
机译:在本文中,我们提出了一种基于Gabor特征的完全Fisher判别算法(GCFD)的新颖特征提取方法。建议的GCFD涉及四个主要步骤:(i)通过对Gabor滤波器组和原始灰度图像进行卷积来提取不同比例和方向的Gabor特征; (ii)完整的Fisher判别算法(CFD)用于减少特征维数并提取所有歧视信息; (iii)最后将特征融合算法和基于欧氏距离的最近邻分类器进行分类。 (iv)仿真结果表明了我们提出的GCFD的有效性。

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