Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18257
Title: A spectral feature based approach for face recognition with one training sample
Authors: Sun, ZL
Lam, KM 
Dong, ZY
Wang, H
Keywords: Face recognition
Fourier transform
Gabor filter
Classifier combination
Linear discriminant analysis
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), 12-15 August 2012, Hong Kong, p. 218-222 How to cite?
Abstract: In this paper, a novel spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm is proposed for face recognition with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method.
URI: http://hdl.handle.net/10397/18257
ISBN: 978-1-4673-2192-1
DOI: 10.1109/ICSPCC.2012.6335726
Appears in Collections:Conference Paper

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