计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 31-35.doi: 10.11896/j.issn.1002-137X.2017.08.006

• 2016 中国计算机图形学会议 • 上一篇    下一篇

基于双邻接图正交近邻保持投影的人脸识别算法

薛潇宇,马小虎   

  1. 苏州大学计算机科学与技术学院 苏州215006,浙江大学计算机辅助设计与图形学国家重点实验室 杭州310058
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受江苏省自然科学基金(BK20141195),浙江大学CAD&CG国家重点实验室基金(A1520)资助

Double Adjacency Graphs Based Orthogonal Neighborhood Preserving Projections for Face Recognition

XUE Xiao-yu and MA Xiao-hu   

  • Online:2018-11-13 Published:2018-11-13

摘要: 正交保持投影(ONPP)是经典的图嵌入降维技术,已经成功地应用到人脸识别中,其保持了高维数据的局部性和整体几何结构。监督的ONPP通过建立同类邻接图来最小化同类局部重构误差,寻找最优的低维嵌入,但是其只使用了类内信息,这会导致异类数据点间的结构不够明显。因此,提出了基于双邻接图的正交近邻保持投影(DAG-ONPP)算法。通过建立同类邻接图与异类邻接图,在数据嵌入低维空间后同类近邻重构误差尽量小,异类近邻重构误差更加明显。在ORL,Yale,YaleB和PIE人脸库上的实验结果表明,与其他经典算法相比,所提方法有效提高了分类能力。

关键词: 监督学习,人脸识别,流型学习,正交近邻保持投影,双邻接图

Abstract: Orthogonal neighborhood preserving projections (ONPP) is a typical graph-based dimensionality reduction technique,which preserves not only the locality but also the local and global geometry of the height dimensional data,and has been successfully applied to face recognition.The supervised ONPP tries to find the optimal embedding of low-dimensional subspace by setting up homogeneous adjacency graphic and minimizing the homogeneous local reconstruction errors.However,it only uses the homogeneous information,which leads to unconspicuous structure of heteroge-neous data.Motivated by this fact,we proposed a novel method called double adjacency graphs based orthogonal neighborhood preserving projections (DAG-ONPP).By introducing homogeneous and heterogeneous neighbor adjacency graphs,the homogeneous reconstructing errors will be as small as possible and the heterogeneous reconstructing errors will be more obvious after data being embedded in low-dimensional subspace.The results of the experiments on the ORL,Yale,YaleB and PIE databases demonstrate that the proposed method can markedly improve the classification ability of the original method and outperforms the other typical methods.

Key words: Supervised learning,Face recognition,Manifold learning,Orthogonal neighborhood preserving projections,Double adjacency graphs

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