Reduced robust facial feature descriptor using DTCWT and PCA

Gauri Agrawal, S. Maurya
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引用次数: 1

Abstract

This paper present a robust reduced facial feature descriptor for face recognition by using dual tree complex wavelet transform and principal component analysis. Proposed approach uses extra dyadic down sampling strategy on coefficient of DT-CWT to reduce the size of feature vector and further without loss of generality principal component analysis is used on reduced feature vector significantly. Geometrical structure in facial image can be represented efficiently and effectively with low redundancy by using extra dyadic down sampling strategy. To extract facial feature this method is robust against the discrepancy of shift and illumination than the DWT. It has been verified experimentally that the proposed method is more dominant to reduce the size of feature vector.
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基于DTCWT和PCA的鲁棒人脸特征描述子
利用对偶树复小波变换和主成分分析,提出了一种鲁棒的简化人脸特征描述子。该方法对DT-CWT的系数采用额外的二进下采样策略来减小特征向量的大小,并且在不损失一般性的情况下对减小的特征向量大量使用主成分分析。采用额外的二进下采样策略,可以高效、低冗余地表示人脸图像中的几何结构。对于人脸特征的提取,该方法比小波变换具有更好的抗偏移和光照差异的鲁棒性。实验证明,该方法在减小特征向量大小方面更有优势。
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