Review Paper on Transform Domains Techniques for Face Recognition

Taif Alobaidi, W. Mikhael
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Abstract

In the last several years, we published several papers to address the problem of Face Identification. The techniques employed in those articles were implemented in transform domains. The Discrete Cosine (DCT) and the Discrete Wavelet (DWT) Transforms were utilized, either combined or individually, to extract features which form the final model for each participant in a given dataset. In this paper, we highlight significant parts of our previous works in order to give a fair comparison among all approaches. The results included here are for the following datasets: ORL, YALE, FERET, FEI, Georgia Tech, and Cropped AR. Features are DWT, DCT, energy-based selected DCT-DWT, and combined DCT-DWT coefficients while the classifier is Euclidean distance, either squared or with power of one.
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人脸识别中的变换域技术综述
在过去的几年里,我们发表了几篇论文来解决人脸识别问题。这些文章中使用的技术是在转换域中实现的。利用离散余弦(DCT)和离散小波(DWT)变换,无论是组合还是单独,来提取特征,形成给定数据集中每个参与者的最终模型。在本文中,我们强调了我们以前工作的重要部分,以便在所有方法之间进行公平的比较。这里包括以下数据集的结果:ORL, YALE, FERET, FEI, Georgia Tech和裁剪AR。特征是DWT, DCT,基于能量的选择DCT-DWT和组合DCT-DWT系数,而分类器是欧氏距离,平方或幂为1。
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