Deep nonlinear metric learning with independent subspace analysis for face verification
Cai, Xinyuan; Wang, Chunheng; Xiao, Baihua; Chen, Xue; Zhou, Ji; Wang Chunheng
Conference Namethe 20th ACM International Conference on Multimedia
Source PublicationACM International Conference on Multimedia
Conference Date2012
Conference PlaceJapan
AbstractFace verification is the task of determining by analyzing face
images, whether a person is who he/she claims to be. It is a very
challenge problem, due to large variations in lighting, background,
expression, hairstyle and occlusion. The crucial problem is to
compute the similarity of two face vectors. Metric learning has
provides a viable solution to this problem. Until now, many metric
learning algorithms have been proposed, but they are usually limited
to learning a linear transformation (i.e. finding a global
Mahalanobis metric). In this brief, we propose a nonlinear metric
learning method, which learns an explicit mapping from the original
space to an optimal subspace, using deep Independent Subspace
Analysis network. Compared to kernel methods, which can
also learn nonlinear transformations, our method is a deep and
local learning architecture, and therefore exhibits more powerful
ability to learn the nature of highly variable dataset. We evaluate
our method on the LFW benchmark, and results show very comparable
performance to the state-of-art methods (achieving 92.28%
accuracy), while maintaining simplicity and good generalization
KeywordIndependent Subspace Analysis Face Verification Deep Learning
Indexed ByEI
Document Type会议论文
Corresponding AuthorWang Chunheng
Recommended Citation
GB/T 7714
Cai, Xinyuan,Wang, Chunheng,Xiao, Baihua,et al. Deep nonlinear metric learning with independent subspace analysis for face verification[C],2012:749-752.
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