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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/2467

Title: DWT/PCA face recognition using automatic coefficient selection
Authors: Nicholl, P
Amira, A
Keywords: Face recognition
Principal component analysis
Discrete wavelet transform
Publication Date: 2008
Publisher: IEEE
Citation: IEEE International Symposium on Design, Electronic, Test and Applications DELTA2008, Hong Kong, January 23-25. pp. 390 - 393
Abstract: In PCA-based face recognition, there is often a trade-off between selecting the most relevant parts of a face image for recognition and not discarding information which may be useful. The work presented in this paper proposes a method to automatically determine the most discriminative coefficients in a DWT/PCA-based face recognition system, based on their inter-class and intra-class standard deviations. In addition, the eigenfaces used for recognition are generally chosen based on the value of their associated eigenvalues. However, the variance indicated by the eigenvalues may be due to factors such as variation in illumination levels between training set faces, rather than differences that are useful for identification. The work presented proposes a method to automatically determine the most discriminative eigenfaces, based on the inter-class and intra-class standard deviations of the training set eigenface weight vectors. The results obtained using the AT&T database show an improvement over existing DWT/PCA coefficient selection techniques.
URI: http://bura.brunel.ac.uk/handle/2438/2467
DOI: http://dx.doi.org/10.1109/DELTA.2008.39
ISBN: 978-0-7695-3110-6
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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