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Title: Gabor-feature hallucination based on generalized canonical correlation analysis for face recognition
Authors: Pong, KH
Lam, KM 
Keywords: Gabor feature
Face recognition
Generalized canonical correlation analysis
Linear regression
Issue Date: 2011
Publisher: IEEE
Source: 2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), 7-9 December 2011, Chiang Mai, p. 1-6 How to cite?
Abstract: In face recognition, image resolution is an important factor which has a great influence on the recognition rate. In traditional face recognition for low-resolution images, face interpolation/super-resolution is usually performed first, and the constructed high-resolution face image will then pass through a face recognition system, which includes feature extraction and classification. To achieve a more efficient and accurate approach, we propose a new method of “Gabor-Feature Hallucination”, which predicts the high-resolution Gabor features from the low-resolution Gabor features directly by using linear regression and Generalized Canonical Correlation Analysis. Then, the low-resolution features in the projected Generalized Canonical Correlation space and the predicted high-resolution Gabor features are adopted for face classification. Our algorithm can therefore avoid performing interpolation/super-resolution and high-resolution Gabor feature extraction. Experimental results show that the proposed method has a superior recognition rate and efficiency to the traditional methods.
ISBN: 978-1-4577-2165-6
DOI: 10.1109/ISPACS.2011.6146163
Appears in Collections:Conference Paper

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