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Author: | Wu, Jing |
Title: | Online Face Recognition with Application to Proactive Augmented Reality |
Publication type: | Master's thesis |
Publication year: | 2010 |
Pages: | ix + 64 Language: eng |
Department/School: | Informaatio- ja luonnontieteiden tiedekunta |
Main subject: | Informaatiotekniikka (T-61) |
Supervisor: | Oja, Erkki |
Instructor: | Koskela, Markus |
Electronic version URL: | http://urn.fi/URN:NBN:fi:aalto-201203131473 |
OEVS: | Electronic archive copy is available via Aalto Thesis Database.
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Location: | P1 Ark Aalto 1575 | Archive |
Keywords: | online face recognition feature extraction classification machine learning illumination normalization recognition accuracy |
Abstract (eng): | 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. |
ED: | 2010-08-17 |
INSSI record number: 40119
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