• DocumentCode
    20415
  • Title

    Face Recognition With Image Sets Using Locally Grassmannian Discriminant Analysis

  • Author

    Haifeng Hu

  • Author_Institution
    Sch. of Inf. Sci. & Technol., Sun Yat-sen Univ., Guangzhou, China
  • Volume
    24
  • Issue
    9
  • fYear
    2014
  • fDate
    Sept. 2014
  • Firstpage
    1461
  • Lastpage
    1474
  • Abstract
    We propose an efficient and robust solution, called locally Grassmannian discriminant analysis (LGDA), for face recognition with image set, where each set contains images belonging to the same subject and typically covering large variations. In our work, by modeling each image set as a nonlinear manifold, linearity-constrained nearest neighborhood clustering is first presented for expressing a manifold by a collection of local linear models (LLMs), each depicted by a subspace. With a proper kernel function defined by canonical correlation between the subspaces, the obtained LLMs can be projected into low-dimensional LGDA embedding space using a set of locally linear transformations. Different from traditional discriminant analysis approaches, LGDA is for multiclass nonlinear discrimination and it can maximize discriminatory power while simultaneously promoting consistency between the multiple local representations of single class objects. A novel accelerated proximal gradient-based learning algorithm is proposed for finding the optimal set of local linear bases. To measure the similarities between the face image sets, three distance criterions are presented, which integrate the distance between the pairs of low-dimensional Grassmannian points from one of the involved manifolds. Comprehensive experiments on the UCSD/Honda, CMU MoBo, and YouTube Celebrities face data sets show that our method consistently outperforms the state of the art.
  • Keywords
    face recognition; image representation; learning (artificial intelligence); pattern clustering; social networking (online); CMU MoBo face data set; LLM; UCSD-Honda face data set; YouTube Celebrities face data sets; canonical correlation; face image sets; face recognition; kernel function; linearity-constrained nearest neighborhood clustering; local linear models; locally Grassmannian discriminant analysis; locally linear transformations; low-dimensional LGDA; multiclass nonlinear discrimination; nonlinear manifold; proximal gradient-based learning algorithm; robust solution; Correlation; Face; Face recognition; Kernel; Linearity; Manifolds; Vectors; Face recognition with image sets; locally Grassmannian discriminant analysis (LGDA); set similarity measure;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems for Video Technology, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1051-8215
  • Type

    jour

  • DOI
    10.1109/TCSVT.2014.2309834
  • Filename
    6756968