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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.contributor.advisorLam, K. M. Kenneth (EIE)-
dc.creatorZeng, Yuhui-
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleAge invariant face recognitionen_US
dcterms.abstractIn this dissertation, different algorithms have been investigated for age-invariant face recognition. The first step is to study and evaluate existing face recognition methods on face images with different ages. Then based on an age-invariant face recognition method, named Multi-Feature Discriminant Analysis (MFDA), further modifications are applied to this method in order to improve the recognition rate on face images with age progression. The modifications include replacing the feature extraction methods with other effective algorithms, experimenting different similarity measurements, and score fusion methods. Since Local Binary Pattern has many variants, which are created to deal with different conditions, some of these variants are selected to replace the original Local Binary Pattern in the MFDA method. For example, the Multi-scale Local Binary Pattern is replaced by Local Ternary Pattern and Cosine similarity is also tested as the feature distance measurement. Experiments were conducted and the results shows that with the FG-NET database, replacing the distance measurement method leads to the greatest improvement in terms of recognition rate.en_US
dcterms.extent45 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.LCSHHuman face recognition (Computer science)en_US
dcterms.LCSHImage analysis -- Data processingen_US
dcterms.LCSHImage processing -- Digital techniquesen_US
dcterms.accessRightsrestricted accessen_US

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