摘要
采用组稀疏表示分类方法时,同类样本同时参与对测试样本的表示,忽略了类内样本间的相关性。提出了一种改进方法,该方法在块正交匹配追踪算法基础上,将样本间的相干系数作为参数,设置适当的阈值,对每次选取的样本进行判别,剔除与测试样本相关性较差的样本,优化算法的重建性能。在Yale B和ORL的数据库上的实验表明,与原有方法相比,改进后的方法得到的识别率较高,实验结果证明了该方法的有效性。
When the group sparse representation is used to face recognition, the same samples take part in representation of the test sample at the same time. The original method ignores the correlation between the samples. To solve this problem, an improved block orthogonal matching pursuit algorithm is presented. The presented algorithm uses the coherent coefficient of the samples as a parameter, setting the proper threshold value to select sample discrimination. Therefore, the reconstruction of the algorithm is optimized. Experiments on the Yale B database and the ORL database show that the recognition rate of improved algorithm is higher than the original one. The experiment results verify the validity of the proposed algorithm.
出处
《计算机工程与应用》
CSCD
2014年第6期175-178,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.61262079)
关键词
人脸识别
组稀疏表示
块正交匹配追踪
face recognition
group sparse representation
block orthogonal matching pursuit
