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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Two-dimensional discriminant transform for face recognition
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Two-dimensional discriminant transform for face recognition

机译:用于人脸识别的二维判别变换

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摘要

This paper develops a new image feature extraction and recognition method coined two-dimensional linear discriminant analysis (2DLDA). 2DLDA provides a sequentially optimal image compression mechanism, making the discriminant information compact into the up-left corner of the image. Also, 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner. 2DLDA is tested and evaluated using the AT&T face database. The experimental results show 2DLDA is more effective and computationally more efficient than the current LDA algorithms for face feature extraction and recognition. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:本文提出了一种基于二维线性判别分析(2DLDA)的图像特征提取与识别新方法。 2DLDA提供了一种顺序优化的图像压缩机制,使判别信息紧凑到图像的左上角。同样,2DLDA提出了一种特征选择策略,以从角落选择最具区分性的特征。使用AT&T人脸数据库对2DLDA进行了测试和评估。实验结果表明,2DLDA在面部特征提取和识别方面比当前的LDA算法更有效,计算效率更高。 (c)2005模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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