Face mis-alignment analysis by multiple-instance subspace

Authors:
Zhiguo Li;Qingshan Liu;Dimitris Metaxas
Affiliations:
Department of Computer Science, Rutgers University;Department of Computer Science, Rutgers University and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences;Department of Computer Science, Rutgers University
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Year:
2007

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Abstract

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.