基于核稀疏表示的人脸人耳融合识别算法的研究  

Research on fusion recognition of human face and ear based on kernel-sparse representation

作　　者：张明[1] 黄炳家[1] 郑秋梅[1] ZHANG Ming;HUANG Bingjia;ZHENG Qiumei(China University of Petroleum(East China),Qingdao 266580,China)

机构地区：[1]中国石油大学(华东),山东青岛266580

出　　处：《现代电子技术》2019年第4期80-84,共5页Modern Electronics Technique

基　　金：国家自然科学基金(61305008);中央高校基本科研业务费专项资金(14CX06008A)~~

摘　　要：针对人脸人耳融合识别算法对图像光照变化、表情变化、拍摄角度变化等鲁棒性不强的问题,将核稀疏表示理论引入到人脸人耳融合识别中,提出基于核稀疏表示的人脸人耳融合识别算法。新算法采用的是能有效降低样本维度的PCA特征提取算法,人脸人耳的特征融合层级选用既能实现冗余信息有效压缩,又能最大程度利用不同模态生物特征可区分性的特征级融合。考虑到不同模态生物特征对最终识别的贡献可能有所不同,该算法采用加权串联融合法,同时测试样本在训练样本中稀疏表示系数的求解采用的是迭代速度比较快的正交匹配追踪算法。与其他识别算法相比,该算法具有非常好的识别性能,并且对人脸人耳图像变化具有很强的鲁棒性。Since the human face and ear fusion recognition algorithm has weak robustness in variations of image illumination,facial expression and shooting angle,the kernel-sparse representation theory is introduced into the human face and ear fusion recognition,and a human face and ear fusion recognition algorithm based on kernel-sparse representation is proposed.The PCA feature extraction algorithm that can effectively reduce sample dimensions is adopted in the algorithm.The feature-level fusion that can not only realize effective compression of redundant information,but also make the best use of the distinguishability of biological features in different modes is selected for the feature fusion level of human faces and ears.The weighted series fusion method is used for the algorithm considering that different modes of biological features may have different contributions to the final recognition.The orthogonal matching pursuit algorithm with fast iteration speed is used to solve the sparse representation coefficient of tested samples in training samples.In comparison with other recognition algorithms,the algorithm has a much better recognition performance,and stronger robustness in variations of human face and ear images.

关 键 词：融合识别 核稀疏表示 特征提取 加权串联融合 正交匹配追踪算法 鲁棒性 

分 类 号：TN820.4-34[电子电信—信息与通信工程] TP391[自动化与计算机技术—计算机应用技术]