改进2DPCA算法在人脸识别中的应用

摘要

随着二维主成分分析法在人脸识别中的应用,许多基于2D的分析方法日益成熟.相比于PCA算法基于向量的特征提取,2DPCA算法是基于矩阵的特征提取.与依赖于特征矩阵的列或特征矩阵的全部矩阵的方法不同,提出了基于特征矩阵行的距离测量方法,该算法与KNN算法进行了结合.通过使用该方法可以缓解2DPCA算法相比于基于主成分分析的算法(PCA)需较多系数的问题.在人脸数据库上的实验结果表明,所提方法的分辨精度比2DPCA方法高,在准确性和存储容量方面超过了2DPCA算法.%With the application of two-dimensional principal component analysis (PCA) in face recognition,a lot of analysis methods based on 2D are becoming more popular.Compared with PCA algorithm based on vector feature extraction,2DPCA algorithm is based on the feature extraction of the matrix.Unlike the methods depending on the columns or all matrix of the eigenmatrix,we proposed an algorithm based on the distance measurement method of the characteristic matrix,and the algorithm is combined with KNN algorithm.By using this method,the shortcoming based on the 2DPCA algorithm compared with algorithm based on principal component analysis (PCA) can alleviate some problems needed to be more coefficient.Experimental results on face database show that the proposed method of distinguish accuracy will increase,is's performance is better than other methods in terms of accuracy and storage capacity.