基于svd的人脸分类比较研究

Yong Xu, Ying-nan Zhao
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引用次数: 3

摘要

本文提出了一种基于奇异值分解的人脸图像分类方法。实验结果表明,该方法优于现有的基于奇异值分解的人脸识别方法。此外,我们还研究了奇异值、左奇异向量和右奇异向量对人脸识别的性能,并评估了现有的基于奇异值分解的人脸识别方法。结果表明,与奇异值和奇异向量相比,基于奇异值分解的重构图像在人脸图像分类中更有用。在实践中,通过基于svd的图像重建过程,我们可以减弱不同成像条件和面部表情对人脸分类的副作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison Study on SVD-Based Face Classification
In this paper, a novel SVD-based method is developed to classify face images. The experimental results show that the novel method outperforms the existing SVD-based face recognition methods. In addition, we investigate the performances on face recognition of singular values, left and right singular vectors generated from SVD and assess the existing SVD-based face recognition methods. It appears that by compared with singular values and singular vectors, SVD-based reconstruction images are more useful in classifying face images. In practice, through the SVD-based image reconstruction process, we may weaken the side effects on face classification of varying imaging conditions and facial expressions.
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