彩色分量流形特征融合的人脸识别

对人脸图像RGB彩色空间三分量的非线性流形嵌入进行了分析，提出一种结合了流形学习技术和图像彩色信息的人脸识别方法。 该方法对人脸图像的彩色三分量分别采用局部线性嵌入（LLE）方法进行特征提取，提取的特征进行归一化处理和特征融合，采用线性判别分析（LDA）增加分类判别性，最后采用k最近邻法（kNN）进行分类。 该方法中提取的特征，能够保持人脸图像数据的非线性结构，同时利用了人脸图像的彩色信息。 对比实验结果表明，利用了彩色信息的三分量流形学习特征融合方法，比Fisherface特征灰度图像和单个彩色分量的人脸识别性能有所改善。 

Color Component Manifold Feature Fusion for Face Recognition

The manifold structure analysis offace image components in RGB color space was given in the paper. A novel face recognition method, which integrates manifold learning technique with the color information, was proposed. In this method, locally linear embedding (LLE) was used for feature extraction. Afterward normalization and fusion was done for the extracted features. LDA was used for improving classification ability. The kNN classifier performed face recognition. The experiment results have shown that the proposed method can improve the performance of both the Fishface intensity image method and the single color component method for face recognition.