Abstract：This paper first discusses the relationship of Principal Component Analysis (PCA) and 2-Dimensional PCA (2DPCA). For 2DPCA eliminating the some covariance information which can be useful for recognition, and PCA’s small sample size problem, an algorithm of feature extraction of face based on the Weighted Variation 2DPCA (WV2DPCA) is proposed. Three sub-parts of the face features are extracted respectively in the method of the variation of 2DPCA, and then are classified according to the weight and the Nearest neighbor theory. The experiments on both of ORL and YALE face bases show improvement in recognition accuracy, fewer coefficients and recognition time over 2DPCA, and this algorithm is also superior to the traditional eigenfaces, ICA and Kernel Eigenfaces in terms of the recognition accuracy.
曾岳, 冯大政. 一种基于加权变形的2DPCA的人脸特征提取方法[J]. 电子与信息学报, 2011, 33(4): 769-774.
Zeng Yue, Feng Da-Zheng. An Algorithm of Feature Extraction of Face Based on the Weighted Variation of 2DPCA. , 2011, 33(4): 769-774.
2011, Vol. 33 
