一种2DDCT与压缩感知结合的人脸识别

针对压缩感知(Compressed Sensing,CS)方法需将图像矩阵转化为向量后进行特征提取,导致数据维数很大,计算复杂等缺点,提出二维离散余弦变换(2DDCT)和压缩感知(Compressed Sensing,CS)相结合的人脸识别方法。新方法首先利用2DDCT将图像变换到频域,压缩人脸图像以去掉人眼不敏感的中频分量与高频分量,这样有效降低了所需特征的维数,减少了计算量;然后通过感知算法进行特征提取得到人脸识别特征,最后运用最近邻分类器完成人脸的识别。在ORL、Yale及Feret人脸数据库的实验结果证明了该算法的有效性与稳健性,特别是在YaleB人脸数据库运用该方法得到了很好的试验结果。

A face recognition algorithm based on combination 2DDCT and compressed sensing

In this paper an improved face recognition algorithm is proposed based on the combination of 2D discrete cosine transform （2DDCT） and Compressed Sensing（CS）because of CS. CS first transforms an image matrix to a vector which caused high dimensionality and computational complexity. In this paper the original face image is processed by 2DDCT to reduce the character dimensions effectively. Then, the image is processed by CS to obtain the face recognition features. Finally, the nearest neighbor （NN） classifier is selected to perform face recognition. The experimental results on ORL , Yale and Feret face databases show that this method is robust and effective in the face recognition, especially in the face database YaleB.