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dc.contributorFaculty of Engineeringen_US
dc.creatorQu, Tong-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/7117-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleLow-resolution face recognitionen_US
dcterms.abstractAmong face-recognition (FR) problems, the identification of low-resolution (LR) face images is still a challenging task. Traditional FR algorithms cannot work satisfactorily in matching LR probe images to high-resolution (HR) gallery images. To perform this matching, there are three standard approaches: (1) down-sample the gallery images and then perform the matching of LR face images; (2) upscale the probe images using super-resolution (SR) methods and then perform the matching of HR face images; and (3) project the LR probe images and the HR gallery images into a common subspace and then perform matching in the subspace. In this project, traditional algorithms based on the first two approaches will first be introduced and evaluated under different resolutions. The four baseline FR algorithms are PCA, also known as eigenfaces, combined PCA and LDA (PCA+LDA, a variant of fisherfaces), the PCA+LDA-based FR algorithm based on Gabor features (G-PCA+LDA), and LGBPHS. The three baseline SR algorithms are the bicubic interpolation, eigentransformation and Coherent Local Linear Reconstruction Super-resolution (CLLR-SR). After that, a coupled-projection method based on Canonical Correlation Analysis (CCA) is proposed and evaluated. Experiments show that the coupled-projection method produces higher identification rates than other FR methods do.en_US
dcterms.extentx, 88 p. : ill. ; 30 cm.en_US
dcterms.issued2013en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Sc.en_US
dcterms.LCSHHuman face recognition (Computer science)en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/7117