Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition
Summary :
In order to overcome the drawback of TVQI and to utilize the property of dimensionality increasing techniques, a novel model for Kernel TV-based Quotient Image employing Gabor analysis is proposed and applied to face recognition with only one sample per subject. To deal with illumination outliers, an enhanced TV-based quotient image (ETVQI) model is first adopted. Then for preprocessed images by ETVQI, a bank of Gabor filters is built to extract features at specified scales and orientations. Lastly, KPCA is introduced to extract final high-order and nonlinear features of extracted Gabor features. According to experiments on the CAS-PEAL face database, our model could outperform Gabor-based KPCA, TVQI and Gabor-based TVQI when they face most outliers (illumination, expression, masking etc.).
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.5 pp.1573-1576
- Publication Date
- 2008/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.5.1573
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
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GaoYun AN, JiYing WU, QiuQi RUAN, "Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 5, pp. 1573-1576, May 2008, doi: 10.1093/ietisy/e91-d.5.1573.
Abstract: In order to overcome the drawback of TVQI and to utilize the property of dimensionality increasing techniques, a novel model for Kernel TV-based Quotient Image employing Gabor analysis is proposed and applied to face recognition with only one sample per subject. To deal with illumination outliers, an enhanced TV-based quotient image (ETVQI) model is first adopted. Then for preprocessed images by ETVQI, a bank of Gabor filters is built to extract features at specified scales and orientations. Lastly, KPCA is introduced to extract final high-order and nonlinear features of extracted Gabor features. According to experiments on the CAS-PEAL face database, our model could outperform Gabor-based KPCA, TVQI and Gabor-based TVQI when they face most outliers (illumination, expression, masking etc.).
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.5.1573/_p
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@ARTICLE{e91-d_5_1573,
author={GaoYun AN, JiYing WU, QiuQi RUAN, },
journal={IEICE TRANSACTIONS on Information},
title={Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition},
year={2008},
volume={E91-D},
number={5},
pages={1573-1576},
abstract={In order to overcome the drawback of TVQI and to utilize the property of dimensionality increasing techniques, a novel model for Kernel TV-based Quotient Image employing Gabor analysis is proposed and applied to face recognition with only one sample per subject. To deal with illumination outliers, an enhanced TV-based quotient image (ETVQI) model is first adopted. Then for preprocessed images by ETVQI, a bank of Gabor filters is built to extract features at specified scales and orientations. Lastly, KPCA is introduced to extract final high-order and nonlinear features of extracted Gabor features. According to experiments on the CAS-PEAL face database, our model could outperform Gabor-based KPCA, TVQI and Gabor-based TVQI when they face most outliers (illumination, expression, masking etc.).},
keywords={},
doi={10.1093/ietisy/e91-d.5.1573},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1573
EP - 1576
AU - GaoYun AN
AU - JiYing WU
AU - QiuQi RUAN
PY - 2008
DO - 10.1093/ietisy/e91-d.5.1573
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2008
AB - In order to overcome the drawback of TVQI and to utilize the property of dimensionality increasing techniques, a novel model for Kernel TV-based Quotient Image employing Gabor analysis is proposed and applied to face recognition with only one sample per subject. To deal with illumination outliers, an enhanced TV-based quotient image (ETVQI) model is first adopted. Then for preprocessed images by ETVQI, a bank of Gabor filters is built to extract features at specified scales and orientations. Lastly, KPCA is introduced to extract final high-order and nonlinear features of extracted Gabor features. According to experiments on the CAS-PEAL face database, our model could outperform Gabor-based KPCA, TVQI and Gabor-based TVQI when they face most outliers (illumination, expression, masking etc.).
ER -
English
