New parallel models for face recognition

Heng Fui Liau, Kah Phooi Seng, Yee Wan Wong, Li Minn Ang

    Research output: Book chapter/Published conference paperConference paperpeer-review

    Abstract

    Subspace methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) extract the features based on space domain. Transformation such as discrete cosine transform (DCT) extracts features based on frequency domain. In this paper, we present two parallel models which intend to utilize the features extracted from frequency and space domain of facial images. Both features are combined under a fusion based scheme. FERET database is chosen to evaluate the performance of the proposed method. Simulation results indicate that the proposed method outperforms other traditional methods and enhance the representation of facial image under low-dimensional features.

    Original languageEnglish
    Title of host publicationProceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007
    Pages306-309
    Number of pages4
    DOIs
    Publication statusPublished - 01 Dec 2007
    Event2007 International Conference on Computational Intelligence and Security, CIS'07 - Harbin, Heilongjiang, China
    Duration: 15 Dec 200719 Dec 2007

    Conference

    Conference2007 International Conference on Computational Intelligence and Security, CIS'07
    Country/TerritoryChina
    CityHarbin, Heilongjiang
    Period15/12/0719/12/07

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