Multi-view face recognition based on tensor subspace analysis and view manifold modeling
NeurocomputingPublished 19 June 2009
Xinbo Gao, Chunna Tian
Citations30
SJR quartileQ1
SJR score1.47
SNIP1.94
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TL;DR
A uniform multi-view face model is achieved to deal with the linearity in identity subspace as well as the nonlinearity in view subspace and a parameter estimation algorithm is developed to solve the view and identity factors automatically.
Abstract
This paper aims to address the face recognition problem with a wide variety of views. We proposed a tensor subspace analysis and view manifold modeling based multi-view face recognition algorithm by improving the TensorFace based one. Tensor subspace analysis is applied to separate the identity and view information of multi-view face images. To model the nonlinearity in view subspace, a novel view manifold is introduced to TensorFace. Thus, a uniform multi-view face model is achieved to deal with the linearity in identity subspace as well as the nonlinearity in view subspace. Meanwhile, a parameter estimation algorithm is developed to solve the view and identity factors automatically. The new face model yields improved facial recognition rates against the traditional TensorFace based method.
Keywords
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