Curvelet-based illumination invariant feature extraction for face recognition

Sue Inn Ch'Ng, Kah Phooi Seng, Li Minn Ang

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

    3 Citations (Scopus)

    Abstract

    This paper presents a curvelet-based illumination invariant feature extraction technique to solve the problem of varying illumination in face recognition. Multiband feature technique is employed to search the decomposed curvelet subbands for subbands which are insensitive to illumination variation. The two best performing subbands are then concatenated to form the Optimal Curvelet Subbands (OCS). To further improve the performance of OSC, histogram equalization is applied to enhance the contrast of the details. The proposed feature extraction method was evaluated on YaleB, EYaleB and AR database. The simulation results obtained shows that the proposed method outperforms its wavelet counterpart and that the extracted subbands are also applicable for other databases.

    Original languageEnglish
    Title of host publicationICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics
    Pages458-462
    Number of pages5
    DOIs
    Publication statusPublished - 01 Dec 2010
    Event2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010 - Kuala Lumpur, Malaysia
    Duration: 05 Dec 201007 Dec 2010

    Conference

    Conference2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010
    Country/TerritoryMalaysia
    CityKuala Lumpur
    Period05/12/1007/12/10

    Fingerprint

    Dive into the research topics of 'Curvelet-based illumination invariant feature extraction for face recognition'. Together they form a unique fingerprint.

    Cite this