Paper
24 June 2005 Face recognition based on eigen-illumination scheme and uncorrelated discriminant analysis
Danghui Liu, LanSun Shen, Kin-Man Lam
Author Affiliations +
Proceedings Volume 5960, Visual Communications and Image Processing 2005; 59602G (2005) https://doi.org/10.1117/12.631561
Event: Visual Communications and Image Processing 2005, 2005, Beijing, China
Abstract
The illumination changes on face images make face recognition a very difficult task. In this paper, a human face representation scheme that is insensitive to illumination variation is proposed in order to deal with the problem. The variations in lighting over human faces are modeled by means of Principal Component Analysis (PCA) on a number of blurred faces under different lighting conditions. Then the 'difference image', which is the difference between the original image and the reconstructed image, is used for face recognition. We also propose an uncorrelated Linear Discriminant Analysis technique for face recognition based on the eigen-illumination representation scheme. This method can obtain the uncorrelated optimal discriminant vectors (UODVs) so that the extracted features are uncorrelated. Experimental results show that the proposed method is effective to deal with varying illimunation problem for face recognition.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Danghui Liu, LanSun Shen, and Kin-Man Lam "Face recognition based on eigen-illumination scheme and uncorrelated discriminant analysis", Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59602G (24 June 2005); https://doi.org/10.1117/12.631561
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Cited by 1 scholarly publication.
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KEYWORDS
Databases

Principal component analysis

Facial recognition systems

Light sources and illumination

Feature extraction

Autoregressive models

Image filtering

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