计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 407-411.doi: 10.11896/jsjkx.210700018

• 图像处理&多媒体技术 • 上一篇    下一篇

基于局部正则二次线性重构表示的人脸识别

黄璞, 杜旭然, 沈阳阳, 杨章静   

  1. 南京审计大学信息工程学院 南京 211815
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 黄璞(huangpu3355@163.com)
  • 基金资助:
    国家自然科学基金(U1831127);江苏省产学研合作项目(DH20190207);南京审计大学(信息工程学院)青年教师开放课题项目(A111010004/012)

Face Recognition Based on Locality Regularized Double Linear Reconstruction Representation

HUANG Pu, DU Xu-ran, SHEN Yang-yang, YANG Zhang-jing   

  1. School of Information Engineering,Nanjing Audit University,Nanjing 211815,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:HUANG Pu,born in 1985,Ph.D,asso-ciate professor.His main research inte-rests include big data analysis,pattern recognition and image processing.
  • Supported by:
    National Natural Science Foundation of China(U1831127),Industry University Research Cooperation Project in Jiangsu Province(DH20190207) and Open Project for Young Teachers of Nanjing Audit University(School of Information Engineering)(A111010004/012).

摘要: 稀疏表示分类器(Sparse Representation based Classification, SRC)求解过程较为复杂,所耗时间较长,协同表示分类器(Collaborative Representation based Classification, CRC)将全体训练样本作为字典来表示待识别样本,字典较大且未考虑样本的类别信息,线性回归分类器(Linear Regression based Classification,LRC)并未考虑不同类样本间的差异,且忽视了样本间的距离关系和潜在的邻域关系。针对以上基于表示学习的图像分类算法的问题和不足,提出了一种基于局部正则二次线性重构表示的人脸识别方法。该方法首先计算待识别样本的类内近邻样本;其次利用类内近邻样本线性重构待识别样本;然后将待识别样本表示成所有类内重构样本的线性组合,同时根据待识别样本与类内重构样本的误差对表示系数施加约束;最后,利用拉格朗日乘子法求解表示系数并根据待识别样本重构误差与表示系数的比值判断待识别样本的类别。在AR,FRGC和FERET数据集上的实验表明,该算法具有优越的识别准确率、时间复杂度和鲁棒性。

关键词: 表示学习, 二次线性重构, 局部正则, 人脸识别

Abstract: The solving process of sparse representation classifier(SRC) is relatively complicated and costs a long time,collaborative representation classifier(CRC) treats all the training samples as the dictionary of unknown samples and the dictionary is large without considering the label information,linear regression classifier(LRC) does not take the differences between inter-class samples into account and ignores the distance information and the neighborhood relations between samples.To address the problems and shortcomings in these representation learning based classification algorithms,this paper proposes a locality regularized double linear reconstruction representation classification method(LRDLRRC) for face recognition.Firstly,LRDLRRC calculates the intra-class nearest neighbors of the query sample and uses the intra-class nearest neighbors to linearly reconstruct the query sample.Then the query sample is represented as a linear combination of all the intra-class reconstruction samples,and the representation coefficient is constrained by the reconstruction error between the query sample and the intra-class reconstruction samples.Finally,the Lagrange multiplier method is applied to solve the representation coefficient,and the classification result of the query sample is determined by the ratio between the reconstruction error and the representation coefficient.Experiments on AR,FRGC and FERET datasets show that the proposed algorithm has superior accuracy,time complexity and strong robustness.

Key words: Double linear reconstruction, Face recognition, Locality regularized, Representation learning

中图分类号: 

  • TP391.4
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