A Regression Model in TensorPCA Subspace for Face Image Super-resolution Reconstruction

A regression model in the tensorPCA subspace is proposed in this paper for face super-resolution reconstruction. An approximate conditional probability model is used for the tensor subspace coefficients and maximum-likelihood estimator gives a linear regression model. The approximation is corrected by adding non-linear component from a RBF-type regressor. Experiments on face images from FERET database validate the algorithm. Although each projection coefficient is estimated by a local estimator, tensorPCA subspace analysis is still a global descriptor, which makes the algorithm have certain ability to deal with partially occluded images.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Junwen Wu, Mohan M. Trivedi
• Keywords: Computer Vision, ICPR 2006, Linear Regression Model, Tensor Subspace Coefficients, TensorPCA Subspace Analysis