1) bilateral two-dimension weighted principle component analysis
双向二维加权主元分析
1.
Facial expression recognition based on bilateral two-dimension weighted principle component analysis;
基于双向二维加权主元分析的人脸表情识别
3) Weighted PCA
加权主元分析
4) Two-dimensional Principal Component Analysis(2DPCA)
二维主元分析
1.
In combination with Wavelet Transform(WT),Two-dimensional Principal Component Analysis(2DPCA) and Ellipsoidal Basis Function(EBF),a fingerprint recognition algorithm based on WT,2DPCA and EBF neural network(EBFNN) is proposed.
结合小波变换(WT)、二维主元分析(2DPCA)和椭球基函数(EBF)特点,提出了一种基于WT、2DPCA和EBF神经网络指纹识别方法。
2.
Combined with Discrete Cosine Transform(DCT) and Two-Dimensional Principal Component Analysis(2DPCA),a novel method in face recognition was presented in this paper.
提出了一种对角离散余弦变换(Discrete Cosine Transform,DCT)和二维主元分析(Two-Dimensional Principal Component Analysis,2DPCA)相结合的人脸识别方法。
3.
Combined with the characteristics of two-dimensional principal component analysis(2DPCA),2DPCA algorithm is applied in face recognition.
结合二维主元分析(two-dimensionalprincipalcomponentanalysis,2DPCA)的特点,将2DPCA算法用于人脸识别。
5) 2DPCA
二维主元分析
1.
Two-dimensional Principle Component Analysis (2DPCA) is used to compute covariance matrix directly according to two-dimensional matrix of face image, which is not be transformed into vector, and computation of eigenvalues and eigen.
二维主元分析(Two-dimensional Principle Component Analysis,2DPCA)无须将人脸图像矩阵转换成向量,直接利用二维人脸图像矩阵求协方差矩阵,其特征值与特征向量的计算得到简化。
2.
Some of face recognition methods based on Principal Component Analysis(PCA),Two-dimensional Principal Component Analysis(2DPCA) and Fisher s Linear Discriminant Analysis(FLDA) are comparatively studied in this paper.
对基于主元分析(PCA)、二维主元分析(2DPCA)和Fisher线性判别分析(FLDA)的人脸识别方法进行了比较研究。
6) Two-dimensional Principle Component Analysis(2DPCA)
二维主元分析(2DPCA)
补充资料：因侵害姓名权、肖像权、名誉权、荣誉权产生的索赔权
因侵害姓名权、肖像权、名誉权、荣誉权产生的索赔权：公民、法人的姓名权、名称权，名誉权、荣誉权、受到侵害的有权要求停止侵害，恢复名誉，消除影响，赔礼道歉，并可以要求赔偿损失。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条