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Abstract-This paper proposes a novel face super-resolution reconstruction (hallucination) technique, color face images reconstruction of RGB space with an error regression model in multi-linear principal component analysis (MPCA). From hallucination framework, many color face images are explained in RGB space. Then, they can be naturally described as tensors or multi-linear arrays. In this way, the error regression analysis is used to find the error estimation which can be obtained from the existing LR in tensor space. The framework consists of learning and hallucinating process. 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 back-projection 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 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. In addition, our experiments on face images from FERET database validate our algorithm Index Terms-MPCA, error regression model

In this paper, the semi-orthogonal multi-linear principal component analysis (MPCA) method has been proposed for color face recognition. Recently, MPCA seems to be an appropriate scheme for dimensionality reduction and feature extraction from color images, handling the color channels in a natural, "holistic" manner. However, it is difficult to develop an effective MPCA method with the orthogonality constraint. Then, the semi-orthogonal MPCA results in more captured variance and more learned features than full orthogonality. In addition, this method can obtain correlation information among different color channels. In these experiments, the facial images in FERET database are used to test for a proposed method. The experimental results also indicate that the proposed method achieve better recognition rates than the well-known methods and it can be suitable for other color models such as HSV, YCbCr and CIELAB. Finally, the proposed recognition method can reduce the computational complexity in the color face recognition process.