Generalized Color Face Hallucination with Linear Regression Model in MPCA
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E94-A No.8 pp.1724-1737
- Publication Date
- 2011/08/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E94.A.1724
- Type of Manuscript
- PAPER
- Category
- Image
Authors
Keyword
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Krissada ASAVASKULKEIT, Somchai JITAPUNKUL, "Generalized Color Face Hallucination with Linear Regression Model in MPCA" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 8, pp. 1724-1737, August 2011, doi: 10.1587/transfun.E94.A.1724.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.1724/_p
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@ARTICLE{e94-a_8_1724,
author={Krissada ASAVASKULKEIT, Somchai JITAPUNKUL, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Generalized Color Face Hallucination with Linear Regression Model in MPCA},
year={2011},
volume={E94-A},
number={8},
pages={1724-1737},
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.},
keywords={},
doi={10.1587/transfun.E94.A.1724},
ISSN={1745-1337},
month={August},}
Copy
TY - JOUR
TI - Generalized Color Face Hallucination with Linear Regression Model in MPCA
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1724
EP - 1737
AU - Krissada ASAVASKULKEIT
AU - Somchai JITAPUNKUL
PY - 2011
DO - 10.1587/transfun.E94.A.1724
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2011
AB - 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.
ER -
English
