Robust face clustering for real-world data
Anantharajah, Kaneswaran (2015) Robust face clustering for real-world data. PhD thesis, Queensland University of Technology.
This thesis has investigated how to cluster a large number of faces within a multi-media corpus in the presence of large session variation. Quality metrics are used to select the best faces to represent a sequence of faces; and session variation modelling improves clustering performance in the presence of wide variations across videos. Findings from this thesis contribute to improving the performance of both face verification systems and the fully automated clustering of faces from a large video corpus.
Impact and interest:
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Tjondronegoro, Dian, Sridharan, Sridha, Fookes, Clinton, & Denman, Simon|
|Keywords:||Face Clustering, Face Verification, Local Inter Session Variability Modelling, Gaussian Mixture Modelling, I-vectors, Session Variation, biometrics, Linear Scoring, Score Normalization|
|Divisions:||Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Institution:||Queensland University of Technology|
|Deposited On:||14 Dec 2015 06:42|
|Last Modified:||14 Dec 2015 06:42|
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