Dual optimal multiband features for face recognition

Y W Wong; K P Seng; Li-Minn Ang

doi:10.1016/j.eswa.2009.09.039

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Dual optimal multiband features for face recognition

Journal article

Peer reviewed

Dual optimal multiband features for face recognition

Y W Wong, K P Seng and Li-Minn Ang

Expert Systems with Applications, Vol.37(4), pp.2957-2962

2010

DOI: https://doi.org/10.1016/j.eswa.2009.09.039

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Abstract

Adaptive fusion

Face recognition

Illumination variation

Multiband features

Neural network

Wavelet packet transform

Illumination and expression variations degrade the performance of a face recognition system. In this paper, a novel dual optimal multiband features method for face recognition is presented. This method aims to increase the robustness of face recognition system to both illumination and expression variations. The wavelet packet transform decomposes image into frequency subbands and the multiband feature fusion technique is incorporated to select optimal multiband feature sets that are invariant to illumination and expression variation separately. Parallel radial basis function neural networks are employed to classify the two sets of feature. The scores generated are then combined and processed by an adaptive fusion mechanism. In this mechanism, the level of illumination variations of the input image is estimated and the weights are assigned to the scores accordingly. Experiments based on Yale, YaleB, AR and ORL databases show that the proposed method outperformed other algorithms.

Details

Title: Dual optimal multiband features for face recognition
Authors: Y W Wong (Author) - University of Nottingham Malaysia Campus
K P Seng (Author) - University of Nottingham Malaysia Campus
Li-Minn Ang (Author) - University of Nottingham Malaysia Campus
Publication details: Expert Systems with Applications, Vol.37(4), pp.2957-2962
Publisher: Elsevier Ltd
Date published: 2010
DOI: 10.1016/j.eswa.2009.09.039
ISSN: 0957-4174; 1873-6793; 0957-4174
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
Language: English
Record Identifier: 99513901102621
Output Type: Journal article

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Web Of Science research areas: Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science