Regularized latent least square regression for cross pose face recognition
Xinyuan Cai; Chunheng Wang; Baihua Xiao; Xue Chen; Ji Zhou; Wang.Chunheng
Conference NameTwenty-Third international joint conference on Artificial Intelligence(IJCAI '13)
Source PublicationInternational Joint Conference on Artificial Intelligence (IJCAI )
Conference Date2013
Conference PlaceBeijing,China
AbstractPose variation is one of the challenging factors for
face recognition. In this paper, we propose a novel
cross-pose face recognition method named as Regularized
Latent Least Square Regression (RLLSR).
The basic assumption is that the images captured
under different poses of one person can be viewed
as pose-specific transforms of a single ideal object.
We treat the observed images as regressor, the ideal
object as response, and then formulate this assumption
in the least square regression framework,
so as to learn the multiple pose-specific transforms.
Specifically, we incorporate some prior knowledge
as two regularization terms into the least square
approach: 1) the smoothness regularization, as the
transforms for nearby poses should not differ too
much; 2) the local consistency constraint, as the
distribution of the latent ideal objects should preserve
the geometric structure of the observed image
space. We develop an alternating algorithm to simultaneously
solve for the ideal objects of the training
individuals and a set of pose-specific transforms.
The experimental results on the Multi-PIE
dataset demonstrate the effectiveness of the proposed
method and superiority over the previous
KeywordFace Recognition
Document Type会议论文
Corresponding AuthorWang.Chunheng
Recommended Citation
GB/T 7714
Xinyuan Cai,Chunheng Wang,Baihua Xiao,et al. Regularized latent least square regression for cross pose face recognition[C],2013:1247-1253.
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