Dr. Krissada Asavaskulkiet Profile

Dr. Krissada Asavaskulkiet

at mahidol university

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Proceedings Article | 10 January 2014 Paper

Performance evaluation in color face hallucination with error regression model in MPCA subspace method

Krissada Asavaskulkiet

Proceedings Volume 9069, 906917 (2014) https://doi.org/10.1117/12.2050929

KEYWORDS: RGB color model, Face hallucination, Error analysis, Principal component analysis, Lawrencium, Image processing, Super resolution, Performance modeling, Databases, Image resolution

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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.

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