PCA型算法在人脸识别中的应用

S. Nedevschi, Ioan Radu Peter, Adina Mandrut
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引用次数: 11

摘要

主成分分析(PCA)是图像识别中应用最广泛的方法之一,因为它在算法的简单性和速度与使用它获得的结果之间取得了很好的平衡。近年来,人们开发了许多PCA的变体:二维PCA、双向二维PCA、扩展二维PCA和扩展二维双向PCA,后者是由本文的前两位作者开发的。在本文中,我们通过考虑E2DPCA和对角PCA之间的混合方法进一步研究。混合方法不仅需要与经典方法大致相同的训练和测试时间,而且对于某些PCA算法变体具有更好的识别精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PCA type algorithm applied in face recognition
One of the widely used approaches in image recognition is principal component analysis (PCA) because of the good balance between the simplicity and speed of the algorithm and the results obtained by using it. In the last years many variants of PCA were developed: two dimensional PCA, two directional two dimensional PCA, extended two dimensional PCA and extended two dimensional two directional PCA, the last one developed by the first two authors of the present paper. In this paper we go further with this study by considering a mixed approach between E2DPCA and diagonal PCA. The mixed approach not only takes approximately the same amount of time for training and testing as the classical approach, but also gives better recognition accuracy for some of the PCA algorithm variants.
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