Face Recognition Using the Wavelet Tree and Two-Dimensional PCA

摘要：

Two-dimensional principal component analysis (2D-PCA) is a fast method for face recognition. Our method (WTMPCA) makes use of 2D-PCA method based on two dimensional Wavelet tree matrices composed of the Wavelet approximation coefficients as opposed to the traditional 2D-PCA, which is grounded on 2D matrices in the image domain. By applying the three-level Wavelet decomposition, the new 2D matrix is made up of the approximation coefficients. The matrices in the Wavelet domain not only contain the whole information of the images, but also extract the local feature. Finally, we use the 2D-PCA method under the new image matrix for face recognition. Experimental results on the ORL and a subset of CAS-PEAL face database show that WTMPCA method achieves 96％ accuracy on face recognition using only one principal component vector.

关键词： Face recognition Wavelet tree WTMPCA

作者: Lin Cao Dengyi Chen Kangning Du Xian Zhu

作者单位: Department of Telecommunication Engineering Beijing Information Science and Technology University Beijing, China 100101

会议类型: 国际会议

会议名称: 2010 International Conference on Measurement and Control Engineering(2010年IEEE测量与控制工程国际会议 ICMCE2010)

会议地点: 成都

会议语种:英文

页码: 167-171

在线出版日期: 2010-11-16（万方平台首次上网日期，不代表论文的发表时间）