Shearlet多方向特征融合与加权直方图的人脸识别算法

摘要

The Shearlet multi-orientation features fusion and weighted histogram are proposed to overcome the disadvantage of Shearlet transform, which has data redundance in extracting features and cannot sparsely represent the global characters. First, Shearlet transform is used to extract the multi-orientation facial features. Then two coding methods are proposed to fuse the features from different directions of the same scale into a single feature, and the fused image is divided into a number of equal-sized nonoverlapping rectangular blocks, weighted fusion of each model using the Shannon entropy theory. Many experiments have been done on the ORL, FERET and YALE face database, which fully proved that this method has more advantages in terms of recognition than the traditional Shearlet.%针对 Shearlet 变换在提取特征数据时存在冗余性以及无法对全局特征进行稀疏表征的缺点，提出了一种Shearlet多方向特征融合与加权直方图的人脸识别算法。首先，对原始图像采用Shearlet变换得到多尺度多方向的人脸特征，然后按照两种编码方式将同一尺度下不同方向的特征进行编码融合，并将融合后的尺度图像划分为若干大小相等的不重叠矩形块，利用Shannon熵理论对各子模式进行加权融合。在ORL、FERET和YALE人脸库中做了多组实验，充分证明该算法相对于传统Shearlet滤波器在分类识别上更具有优势。