• DocumentCode
    2995348
  • Title

    Robust Sparse 2DPCA and Its Application to Face Recognition

  • Author

    Meng, Jicheng ; Zheng, Xiaolong

  • Author_Institution
    Sch. of Autom. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
  • fYear
    2012
  • fDate
    21-23 May 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    This paper proposes robust sparse 2DPCA (RS2DPCA) that makes the best of semantic, structural information and suppresses outliers. The proposed RS2DPCA combines the advantages of sparsity, 2D data format and L1-norm. To assess the performance of RS2DPCA in face recognition, experiments are performed on two famous face databases, i.e. Yale, and FERET, and the experimental results indicate that the proposed RS2DPCA outperform the same class of algorithms, such as RSPCA, 2DPCAL1.
  • Keywords
    face recognition; principal component analysis; 2D data format; FERET; L1-norm; RS2DPCA; Yale; face recognition; robust sparse 2DPCA; Computer vision; Databases; Educational institutions; Face; Face recognition; Principal component analysis; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Photonics and Optoelectronics (SOPO), 2012 Symposium on
  • Conference_Location
    Shanghai
  • ISSN
    2156-8464
  • Print_ISBN
    978-1-4577-0909-8
  • Type

    conf

  • DOI
    10.1109/SOPO.2012.6270566
  • Filename
    6270566
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2995348