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Circuits, Systems, and Signal Processing

, Volume 37, Issue 5, pp 2045–2073 | Cite as

Face Recognition Employing DMWT Followed by FastICA

  • Ahmed Aldhahab
  • Wasfy B. Mikhael
Article

Abstract

Face recognition becomes a challenging topic in several fields since images of faces are varied by changing illuminations, facial rotations, facial expressions, etc. In this paper, two dimensional discrete multiwavelet transform (2D DMWT) and fast independent component analysis (FastICA) are proposed for face recognition. Preprocessing, feature extraction, and classification are the main steps in the proposed system. In the preprocessing step, each pose in the database is divided into six parts to reduce the effect of unnecessary facial features and highlight the local features in each part. For feature extraction, the 2D DMWT is applied to each part for dimensionality reduction and features extraction. This results in two facial representations. Then FastICA followed by \(\ell _2\)-norm is applied to each representation, which produces six and three different techniques for the first and second representation, respectively. This results in features that are more discriminating, less dependent, and more compressed. In the recognition step, the resulted compressed features from the two representations are fed to a neural network-based classifier for training and testing. The proposed techniques are extensively evaluated using five databases, namely ORL, YALE, FERET, FEI, and LFW, which have different facial variations, such as illuminations, rotations, facial expressions, etc. The results are analyzed using K-fold cross-validation. Sample results and comparison with a large number of recently proposed approaches are provided. The proposed approach is shown to yield significant improvement compared with the other approaches.

Keywords

Discrete wavelet and multiwavelet transform Independent component analysis (ICA) FastICA Neural network Face recognition 

Notes

Acknowledgements

This work was supported by the Iraqi government scholarship (HCED). The authors acknowledge the valuable comments and feedback from their colleague “Dr. George Atia”.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of Central FloridaOrlandoUSA
  2. 2.Department of Electrical EngineeringUniversity of BabylonHillaIraq

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