This paper proposes a novel face super-resolution reconstruction (hallucination) technique for YCbCr color space.
The underlying idea is to learn with an error regression model and multi-linear principal component analysis (MPCA).
From hallucination framework, many color face images are explained in YCbCr space. To reduce the time complexity of
color face hallucination, we can be naturally described the color face imaged as tensors or multi-linear arrays. In addition,
the error regression analysis is used to find the error estimation which can be obtained from the existing LR in tensor
space. In learning process is from the mistakes in reconstruct face images of the training dataset by MPCA, then finding
the relationship between input and error by regression analysis. In hallucinating process uses normal method by backprojection
of MPCA, after that the result is corrected with the error estimation. In this contribution we show that our
hallucination technique can be suitable for color face images both in RGB and YCbCr space. By using the MPCA
subspace with error regression model, we can generate photorealistic color face images. Our approach is demonstrated
by extensive experiments with high-quality hallucinated color faces. Comparison with existing algorithms shows the
effectiveness of the proposed method.