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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A GABOR FEATURE BASED HORIZONTAL AND VERTICAL DISCRIMINANT FOR FACE VERIFICATION
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A GABOR FEATURE BASED HORIZONTAL AND VERTICAL DISCRIMINANT FOR FACE VERIFICATION

机译：基于Gabor特征的水平和垂直判别器用于人脸验证

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摘要

In this paper, a novel discriminant analysis method for a Gabor-based image feature extraction and representation is proposed and then implemented. The horizontal and vertical two-dimensional principal component analysis (HV-2DPCA) is directly applied to a Gabor face to reduce the redundant information and preserve a bi-directional characteristic as well. It is followed by an enhanced Fisher linear discriminant model (EFM) generating a low-dimensional feature representation with enhanced discrimination power. By the most discriminant features, different types of classes of training samples are made widely apart and the same category classes are made as compact as possible. This novel algorithm is designated as the horizontal and vertical enhanced Gabor Fisher discriminant (HV-EGF) in this paper. By use of various dimensions of features as well as various numbers of training samples, our experiments indicate that the proposed HV-EGF method provides a superior recognition accuracy relative to those by the Fisher linear discriminant (FLD), the EFM and the Gabor Fisher classifier (GFC) methods. In our proposal, the recognition accuracies up to 99.0% and 97.7% are reached with images of features dimensions 38 × 38 × 2 and 10 × 10 × 2 on the ORL and the Yale databases, respectively.
机译：提出并实现了一种基于Gabor的图像特征提取与表示的判别分析新方法。水平和垂直二维主成分分析（HV-2DPCA）直接应用于Gabor面，以减少冗余信息并保留双向特性。随后是增强的Fisher线性判别模型（EFM），该模型生成具有增强的辨别力的低维特征表示。通过最有区别的特征，可以将不同类型的训练样本类别广泛分开，并且将相同类别的类别尽可能紧凑。本文将该新算法称为水平和垂直增强Gabor Fisher判别式（HV-EGF）。通过使用特征的各个维度以及不同数量的训练样本，我们的实验表明，相对于Fisher线性判别器（FLD），EFM和Gabor Fisher分类器，HV-EGF方法具有更高的识别精度。 （GFC）方法。在我们的建议中，在ORL和Yale数据库上，特征尺寸分别为38×38×2和10×10×2的图像分别达到了99.0％和97.7％的识别精度。

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