Generalized Color Face Hallucination with Linear Regression Model in MPCA

Abstract

This paper proposes a novel hallucination technique for color face image reconstruction in the RGB, YCbCr, HSV and CIELAB color systems. Our hallucination method depends on multilinear principal component analysis (MPCA) with a linear regression model. In the hallucination framework, many color face images are expressed in color spaces. These images can be naturally described as tensors or multilinear arrays. This novel hallucination technique can perform feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. In our experiments, we used facial images from the FERET database to test our hallucination approach which is demonstrated by extensive experiments with high-quality hallucinated color face images. The experimental results show that a correlation between the color channel and the proposed hallucination method can reduce the complexity in the color face hallucination process.