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An Illumination Invariant Bimodal Method Employing Discriminant Features for Face Recognition

JiYing WU, QiuQi RUAN, Gaoyun AN

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JiYing WU
Institute of Information Science, Beijing Jiaotong University
QiuQi RUAN
Institute of Information Science, Beijing Jiaotong University
Gaoyun AN
Institute of Information Science, Beijing Jiaotong University

Corresponding author

Keywords:enhanced gray level image,illumination invariant image,discriminant feature extraction,image fusion

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2009 Volume E92.DIssue 2Pages 365-368

DOIhttps://doi.org/10.1587/transinf.E92.D.365

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Published: February 01, 2009Received: September 04, 2008Available on J-STAGE: February 01, 2009Accepted: -Advance online publication: -Revised: -

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Article overview

Abstract

A novel bimodal method for face recognition under low-level lighting conditions is proposed. It fuses an enhanced gray level imageand 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.

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