基于子模式双向二维主成分分析的人脸识别

为了减轻人脸姿态、表情和光照条件等因素变化对识别率的影响,采用了一种子模式双向二维主成分分析(Sp-(2D)2PCA)的人脸识别新方法。该方法通过对原图像进行分块处理,能有效地抽取原图像的局部特征;同时,通过采用(2D)2PCA对分块得到的子图像矩阵直接进行特征抽取,避免了矩阵向量间的转化,能精确地计算协方差矩阵的特征向量,并能有效地降低特征维数。试验结果表明,在姿态、表情和光照条件变化的情况下,Sp-(2D)2PCA都具有较好的识别性能。

Face recognition based on sub-pattern two-directional 2DPCA

To reduce the impacts on face recognition rate coursed by variations of pose,expression and illumination,a new face recognition approach based on sub-pattern two-directional 2DPCA is adopted.By dividing the original images into blocks,the approach can efficiently extract the local discriminant features of these images.At the same time,the two-directional 2DPCA((2D)2PCA)method is used to extract the features on these blocks straightly,which avoids the conversion between matrix and vector.Therefore,the method...