Optimized Gabor Features For Facial Recognition System
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
Editura Universitatii din Pitesti
Abstract
Feature extraction is a significant process in any pattern recognition, computer vision
and image processing. Among several feature extraction techniques like Fisher Linear
Discriminant Analysis (FLDA), Principal Component Analysis (PCA), Elastic Bunch Graph
Matching (EBGM) and Local Binary Pattern (LBP), Gabor-filters possess the ability of obtaining
multi-orientation features from a facial image at several scales with the derived information being
of local nature. Its optimal functionality in facial recognition is linked to its biological importance
(similarity to the receptive fields of simple cells in primary visual cortex) and computational
properties (optimal for calculating local spatial frequencies). Despite all the outstanding
properties of Gabor-filters, this technique suffers high feature dimensionality. This paper
addresses the problem of high feature dimensionality by application of Ant Colony Optimization
meta-heuristic algorithm for feature selection of relevant and optimal features. Two face image
databases; Olivetti Research Laboratory (ORL) Database and Locally Acquired Face Image
Database (LAFI) are used to evaluate the performance of the proposed facial recognition model.
The final experimental results showed better performance.
Description
Keywords
Gabor-filters, feature extraction, pattern recognition, visual cortex, Gabor features
Citation
Aro, T.O., Abikoye, O.C. & Bajeh, A.O. (2018): Optimized Gabor Features for Facial Recognition System. The University of Pitesti Scientific Bulletin, Series: Electronics and Computers Science, 18(1); 15-26, Published by Editura Universitatii din Pitesti.