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首页> 外文会议>2009年中国控制与决策会议（2009 Chinese Control and Decision Conference）论文集 >An Approach for Face Recognition Based on Fusion of DTCWT and Manifold Regularized Orthogonal Discriminant Analysis
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An Approach for Face Recognition Based on Fusion of DTCWT and Manifold Regularized Orthogonal Discriminant Analysis

机译：基于DTCWT与流形正则化正交判别分析融合的人脸识别方法

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摘要

In this paper, a novel subspace learning method called Manifold Regularized Orthogonal Discriminant Analysis (MRODA) is first proposed. Based on within-class local geometry preservation and Least Square regression framework for LDA, MRODA can encode both the local geometry and discriminant structures of face data manifolds, and can address the small sample size problem through pseudo-inverse resolution. The transform vectors are orthogonalized to improve their discriminatory performance. Based on the selected Dual-Tree Complex Wavelet Transform features, an approach for face recognition based on the fusion of spatial and frequency features is developed. Experimental results on ORL, Yale and AR face databases show the effectiveness of the proposed approach.
机译：本文首先提出了一种新的子空间学习方法，称为流形正则化正交判别分析（MRODA）。基于LDA的类内局部几何形状保留和最小二乘回归框架，MRODA可以对面部数据流形的局部几何形状和判别结构进行编码，并可以通过伪逆分辨率解决小样本量问题。将变换矢量正交化以提高其区分性能。基于选定的双树复数小波变换特征，开发了一种基于空间和频率特征融合的人脸识别方法。在ORL，Yale和AR人脸数据库上的实验结果证明了该方法的有效性。

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