dc.contributor.authorLu, Jiwen
dc.date.accessioned2011-12-06T02:57:32Z
dc.date.accessioned2017-07-23T08:33:38Z
dc.date.available2011-12-06T02:57:32Z
dc.date.available2017-07-23T08:33:38Z
dc.date.copyright2011en_US
dc.date.issued2011
dc.identifier.citationLu, J. (2011). Enhanced linear subspace methods for face and gait analysis. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/46438
dc.description.abstractFeature extraction has been extensively investigated and discussed in computer vision and pattern recognition literature over the past three decades. It has particularly attracted more and more attention in recent years due to the increasing demands for developing real-world human computer interaction systems. While a large number of feature extraction algorithms have been proposed in the literature and some of them have achieved reasonably good performance in many real world applications, such as face recognition, gait recognition, facial expression recognition and human age estimation, there is still some room for further improvement to address the challenges of these methods. In this thesis, we propose various enhanced linear subspace algorithms and apply them to face and gait feature extraction to demonstrate their efficacy and superiority over state-of-the-art methods. Specifically, we propose four new subspace learning approaches, including double weighted subspace learning, parametric regularized subspace learning, cost-sensitive subspace learning, and subspace learning with limited number of training samples. Lastly, we also apply multi-label subspace learning techniques for human age estimation. The above-mentioned methods have been successfully applied to several computer vision applications, such as face recognition, facial expression recognition and gait-based human age estimation. Experimental results are presented to demonstrate the efficacy of the proposed methods.en_US
dc.format.extent144 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometricsen_US
dc.titleEnhanced linear subspace methods for face and gait analysisen_US
dc.typeThesis
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.supervisorTan Yap Peng (EEE)en_US
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en_US


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