A face authentication scheme based on Affine-SIFT (ASIFT) and Structural Similarity (SSIM)

Lifang Wu, Peng Zhou, Shuqin Liu, Xiuzhen Zhang, Emanuele Trucco

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    In this paper, we propose a novel face authentication approach based on affine scale invariant feature transform (ASIFT) and structural similarity (SSIM). The ASIFT descriptor defines key points which are used to match the gallery and probe face images. The matched pairs of key points are filtered based on the location of points in the gallery face image. Then the similarity between sub-images at a preserved pair of matched points is measured by Structural Similarity (SSIM). A mean SSIM (MSSIM) at all pairs of points is computed for authentication. The proposed approach is tested on FERET, CMU-PIE and AR databases with only one image for enrollment. Comparative results on the AR database show that our approach outperforms state-of-the-art approaches.
    Original languageEnglish
    Title of host publicationBiometric Recognition
    Subtitle of host publication7th Chinese Conference, CCBR 2012, Guangzhou, China, December 4-5, 2012. Proceedings
    EditorsWei-Shi Zheng
    Place of PublicationBerlin
    PublisherSpringer
    Pages25-32
    Number of pages8
    ISBN (Electronic)9783642351365
    ISBN (Print)9783642351358
    DOIs
    Publication statusPublished - 2012
    Event7th Chinese Conference on Biometric Recognition - Guangzhou, China
    Duration: 4 May 20125 May 2012

    Publication series

    NameLecture notes in computer science
    PublisherSpringer
    Volume7701
    ISSN (Print)0302-9743

    Conference

    Conference7th Chinese Conference on Biometric Recognition
    Abbreviated titleCCBR 2012
    CountryChina
    CityGuangzhou
    Period4/05/125/05/12

    Fingerprint Dive into the research topics of 'A face authentication scheme based on Affine-SIFT (ASIFT) and Structural Similarity (SSIM)'. Together they form a unique fingerprint.

    Cite this