针对单训练样本情况下的人脸识别问题，提出一种虚拟样本扩展方法. 利用光照模板映射将单一样本扩展为一组虚拟样本，从而增强单训练样本的分类信息. 采用主成分分析（principal component analysis, PCA）对扩展的虚拟样本进行降维，并用Fisher 鉴别变换作二次特征抽取，然后用最近邻分类器识别人脸图像. 所提方法在人脸图像库Yale B 和Extended Yale B 上进行试验，用PCA+LDA 方法把扩展图像作为训练集对测试图像进行特征提取和识别. 相对于以单样本图像为训练集的PCA 特征提取，该方法显著提高了识别率.

 A method of reference model illumination mapping is proposed to deal with the problem of one sample per person. To enhance the classification information of single training sample, we extend virtual images generated from the given single training image. Using discrete wavelet transform (DWT), the low-frequency band is processed to map the illumination information from the reference model to create a virtual sample. Principle-component-analysis plus linear-discriminant-analysis (PCA+LDA) is performed on the virtual training set to extract features. Experiments are performed on the Yale B and extended Yale B facial image database. The results show that, compared with the PCA feature extraction with single sample, recognition rate is significantly higher using the proposed method.