An Illumination Invariant Bimodal Method Employing Discriminant Features for Face Recognition
Summary :
A novel bimodal method for face recognition under low-level lighting conditions is proposed. It fuses an enhanced gray level image and an illumination-invariant geometric image at the feature-level. To further improve the recognition performance under large variations in attributions such as poses and expressions, discriminant features are extracted from source images using the wavelet transform-based method. Features are adaptively fused to reconstruct the final face sample. Then FLD is used to generate a supervised discriminant space for the classification task. Experiments show that the bimodal method outperforms conventional methods under complex conditions.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.2 pp.365-368
- Publication Date
- 2009/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.365
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
JiYing WU, QiuQi RUAN, Gaoyun AN, "An Illumination Invariant Bimodal Method Employing Discriminant Features for Face Recognition" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 2, pp. 365-368, February 2009, doi: 10.1587/transinf.E92.D.365.
Abstract: A novel bimodal method for face recognition under low-level lighting conditions is proposed. It fuses an enhanced gray level image and an illumination-invariant geometric image at the feature-level. To further improve the recognition performance under large variations in attributions such as poses and expressions, discriminant features are extracted from source images using the wavelet transform-based method. Features are adaptively fused to reconstruct the final face sample. Then FLD is used to generate a supervised discriminant space for the classification task. Experiments show that the bimodal method outperforms conventional methods under complex conditions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.365/_p
Copy
@ARTICLE{e92-d_2_365,
author={JiYing WU, QiuQi RUAN, Gaoyun AN, },
journal={IEICE TRANSACTIONS on Information},
title={An Illumination Invariant Bimodal Method Employing Discriminant Features for Face Recognition},
year={2009},
volume={E92-D},
number={2},
pages={365-368},
abstract={A novel bimodal method for face recognition under low-level lighting conditions is proposed. It fuses an enhanced gray level image and an illumination-invariant geometric image at the feature-level. To further improve the recognition performance under large variations in attributions such as poses and expressions, discriminant features are extracted from source images using the wavelet transform-based method. Features are adaptively fused to reconstruct the final face sample. Then FLD is used to generate a supervised discriminant space for the classification task. Experiments show that the bimodal method outperforms conventional methods under complex conditions.},
keywords={},
doi={10.1587/transinf.E92.D.365},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - An Illumination Invariant Bimodal Method Employing Discriminant Features for Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 365
EP - 368
AU - JiYing WU
AU - QiuQi RUAN
AU - Gaoyun AN
PY - 2009
DO - 10.1587/transinf.E92.D.365
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2009
AB - A novel bimodal method for face recognition under low-level lighting conditions is proposed. It fuses an enhanced gray level image and an illumination-invariant geometric image at the feature-level. To further improve the recognition performance under large variations in attributions such as poses and expressions, discriminant features are extracted from source images using the wavelet transform-based method. Features are adaptively fused to reconstruct the final face sample. Then FLD is used to generate a supervised discriminant space for the classification task. Experiments show that the bimodal method outperforms conventional methods under complex conditions.
ER -
English
