提出了利用两类投影抽取特征、用并行策略融合特征进行人脸识别的新方法。先用一维的基于向量的投影抽取一组特征，再用基于二维的图像投影的方法抽取一组特征，用复向量将样本的两组特征向量组合在一起，在复向量空间分析主分量(CPCA)，抽取人脸图像的鉴别特征。在FERET人脸库上的实验结果表明，该方法的识别性能比用单个特征有10%左右的提高。

A novel face recognition algorithm based on two kinds of projection is presented in this paper. First, the two dimension principal component analysis (2DPCA) is used to extract one group of features, denoted by α. Second, the fisher linear discriminant analysis (LDA) , or fisherfaces, is used for extracting another group of features, denoted by β.After being standardized, the two kinds of features are combined together in the form of the complex vector α+iβ. Then the fusion features in the complex feature space is extracted by using complex PCA (CPCA). The proposed algorithm is evaluated by using the FERET face database at three different resolutions. The experimental results indicate that the proposed method can achieve about 10% higher recognition accurate rate than 2DPCA and LDA, while only using 28 features for each sample.