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ACCV
2007
Springer

Face Mis-alignment Analysis by Multiple-Instance Subspace

10 years 3 months ago
Face Mis-alignment Analysis by Multiple-Instance Subspace
In this paper, we systematically study the effect of poorly registered faces on the training and inferring stages of traditional face recognition algorithms. We then propose a novel multiple-instance based subspace learning scheme for face recognition. In this approach, we iteratively update the subspace training instances according to diverse densities, using class-balanced supervised clustering. We test our multiple instance subspace learning algorithm with Fisherface for the application of face recognition. Experimental results show that the proposed learning algorithm can improve the robustness of current methods with poorly aligned training and testing data.
Zhiguo Li, Qingshan Liu, Dimitris N. Metaxas
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ACCV
Authors Zhiguo Li, Qingshan Liu, Dimitris N. Metaxas
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