Face Recognition Employing Dmwt Followed By Fastica

Keywords

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

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 ℓ2-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.

Publication Date

5-1-2018

Publication Title

Circuits, Systems, and Signal Processing

Volume

37

Issue

5

Number of Pages

2045-2073

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s00034-017-0653-z

Socpus ID

85044303215 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85044303215

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