特征采样和特征融合的子图像人脸识别方法

Sub-Image Method Based on Feature Sampling and Feature Fusion for Face Recognition

提出一种基于特征采样和特征融合的子图像人脸识别方法(RS-SpCCA).首先,对子图像进行特征采样;然后,将全局特征和采样后的特征使用CCA进行信息融合,以获取包含全局特征和局部特征的相关特征;最后,在相关特征上构建分量分类器.在该方法中,特征采样是为了构建更多且多样的分量分类器;而引入特征融合思想是为了充分利用图像的全局特征.AR,Yale和ORL这3个数据库上的实验结果表明,基于特征采样和特征融合的子图像方法(RS-SpCCA)优于单纯的信息融合方法(SpCCA)和特征采样方法(Semi-RS).

In this paper, a sub-image method based on feature sampling and feature fusion （called as RS_SpCCA） is proposed. RS_SpCCA first performs a random subspace method in sub-images which are partitioned in a deterministic way. Then, the method obtains correlation features by fusing sampled features and global feature extracted by certain feature extraction method and finally, constructs component classifiers on corrleation features. In this method, the purpose of sampling feature is to construct more diverse component classifiers, and the purpose of the fusing feature is to make good use of the global information. The experimental results on AR, Yale and ORL three face image databases show that sub-image method based on feature sampling and feature fusion （RS SpCCA） is superior to both SpCCA and Semi-RS which only use feature sampling or feature fusion.