基于WT-W2D2DPCA的人脸识别算法研究

人脸识别过程中,针对二维主成分分析(2DPCA)算法在特征提取和数据降维上存在的问题,本文首先引入双向二维主成分分析(2D2DPCA)算法,该算法同时考虑图像行与列方向上的信息.考虑到人脸图像存在信息冗余而影响识别率的问题,于是本文提出一种基于小波加权双向二维主成分分析(WT-W2D2DPCA)的人脸识别算法.该算法首先采用二级小波分解对人脸图像进行预处理,提取其低频部分;然后根据人脸图像的特性,将低频部分进行奇偶分解,并引入加权思想,重组低频人脸图像,最后在ORL人脸数据库上进行双向二维主成分分析.实验结果表明,该方法不仅克服了传统2DPCA系数矩阵大的问题,而且得到了比传统的2DPCA、2D2DPCA算法更好的识别效果.

Face Recognition Based on WT-W2D2DPCA Algorithm

To solve the problem of feature extraction and dimensional reduction of 2DPCA algorithm in face recognition, two-directional two-dimensional principal component analysis （2D2DPCA） algorithm is adopted simultaneously in the row and column directions of face images. The recognition rate may be influenced by information redundancy of face images. An algorithm is proposed based on the wavelet transibrmation-weighted two-directional two-dimensional principle component analysis （WT-W2D2DPCA） for face recognition. Firstly, the two-level wavelet transformation is used to preprocess human facial image, and low frequency subband is extracted. Considering the even and odd symmetrical characteristics of human face, it is decomposed into even and odd symmetrical images, and the new low frequency facial image is reconstructed. Finally, the WT-W2D2DPCA is used in ORL face database. Experimental results show that not only the problem of the great coefficient matrix of the traditional 2DPCA is overcome, but also better performance than 2D2DPCA algorithm and 2DPCA algorithm after the weighted processing is achieved.