An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

Jun Huang, Kehua Su, Jamal El-Den, Tao Hu, Junlong Li

    Research output: Contribution to journalArticle

    6 Downloads (Pure)

    Abstract

    We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA). Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while the K nearest neighbor (KNN) is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.
    Original languageEnglish
    Pages (from-to)1-12
    Number of pages12
    JournalMathematical Problems in Engineering
    Volume2014
    DOIs
    Publication statusPublished - 2014

    Fingerprint Dive into the research topics of 'An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition'. Together they form a unique fingerprint.

  • Cite this