Optimized Gabor Features For Facial Recognition System

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Editura Universitatii din Pitesti


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.



Gabor-filters, feature extraction, pattern recognition, visual cortex, Gabor features


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.