摘要： 人脸重要特征部位所在分块应具有更大的分类表决权,而传统图像分块算法往往忽略该问题。为此,提出一种自动加权稀疏表示算法。通过引入一个带重叠的滑动窗口计算分块像素点的均方差,并给出自动加权策略,对每个分块在最终分类中的权重进行度量。在公共数据集上的实验结果表明,与常用的分类算法及分块算法相比,该算法无论是在对最小残差法还是投票法进行加权时,均能提高识别准确率。

关键词: 局部特征凸显, 均方差度量, 分块加权, 稀疏表示, 人脸识别

Abstract: Image segmentation algorithm ignores the image patch which contains the important local facial feature should have bigger weights of voting in the final classification.To solve this problem,this paper proposes an automatic weighting sparse representation algorithm.Through using sliding windows with overlapping,the mean square error value is calculated,and an automatic weighting strategy is given to measure the weight of each patch in the final classification.Experimental results on the public data set show that,compared with the commonly used classification algorithm and block algorithm,the proposed algorithm can get higher recognition rate in face recognition wherever it gives weights to the minimum error or the biggest votes.

Key words: local feature highlight, mean square deviation, block weighting, sparse representation, face recognition

中图分类号: 

• TP301.6