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Face Recognition Employing DMWT Followed by FastICA

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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.

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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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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, 32816, USA

    Ahmed Aldhahab & Wasfy B. Mikhael

  2. Department of Electrical Engineering, University of Babylon, Hilla, Babylon, Iraq

    Ahmed Aldhahab

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  1. Ahmed Aldhahab
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Correspondence to Ahmed Aldhahab.

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Aldhahab, A., Mikhael, W.B. Face Recognition Employing DMWT Followed by FastICA. Circuits Syst Signal Process 37, 2045–2073 (2018). https://doi.org/10.1007/s00034-017-0653-z

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