Online Face Recognition with Application to Proactive Augmented Reality

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Koskela, Markus
dc.contributor.author Wu, Jing
dc.date.accessioned 2012-03-12T07:04:41Z
dc.date.available 2012-03-12T07:04:41Z
dc.date.issued 2010
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/3242
dc.description.abstract Recently, more and more researchers have concentrated on the research of video-based face recognition. The topic of this thesis is online face recognition with application to proactive augmented reality. We intend to solve online single-image and multiple-image face recognition problems when the influence of illumination variations is introduced. First, three machine learning approaches are utilized in single-image face recognition: PCA-based, 2DPCA-based, and SVM-based approaches. Illumination variations are big obstacles for face recognition. The next step in our approach therefore involves illumination normalization. Image preprocessing (AHE+RGIC) and invariant feature extraction (Eigenphases and LBP) methods are employed to compensate for illumination variations. Finally, in order to improve the recognition performance, we propose several novel algorithms to multiple-image face recognition which consider the multiple images as query data for subsequent classification. These algorithms are called MIK-NN, MMIK-NN and Kmeans+Muliple K-NN. In conclusion, the simulation experiment results show that the LBP+x2-based method efficiently compensates for the illumination effect and MMIK-NN considerably improves the performance of online face recognition. en
dc.format.extent ix + 64
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Aalto-yliopisto fi
dc.publisher Aalto University en
dc.title Online Face Recognition with Application to Proactive Augmented Reality en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Informaatio- ja luonnontieteiden tiedekunta fi
dc.subject.keyword online face recognition en
dc.subject.keyword feature extraction en
dc.subject.keyword classification en
dc.subject.keyword machine learning en
dc.subject.keyword illumination normalization en
dc.subject.keyword recognition accuracy en
dc.identifier.urn URN:NBN:fi:aalto-201203131473
dc.type.dcmitype text en
dc.programme.major Informaatiotekniikka fi
dc.programme.mcode T-61
dc.type.ontasot Diplomityö fi
dc.type.ontasot Master's thesis en
dc.contributor.supervisor Oja, Erkki


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