The Gabor-filter approach has been extensively used in the recognition of patterns most especially in the extraction of features during image processing. Gabor filters usefulness explored in face recognition is traceable to its computational properties and biological relevance. Despite all the distinct characteristics of Gabor filters, it suffers from high feature dimensionality. This has led majorly to computational problems in any Gabor-based facial recognition model. The paper presents modified Gabor features for face recognition by introducing a meta-heuristics optimization algorithm using the Ant Colony Optimization Algorithm (ACO) to obtain relevant and optimal features from huge Gabor features. Kernels of Support Vector Machines (SVM); Linear SVM Kernel (LSVMK), Polynomial SVM Kernel (PSVMK), Sigmoid SVM (SSVMK) and Gaussian SVM Kernel (GSVMK) were employed for the classification of face images to either matched or mismatched. Two datasets were used for the evaluation of the system, they include: the Olivetti Research Laboratory (ORL) database and the acquired Africa face image database (ABFI). All the Kernelized SVMs produced an effective output in terms of training time, classification accuracy and error rate. Experimental results showed the lowest training time of 7.3195s was obtained in GSVMK for ABFI face image dataset, non-optimized Gabor feature gave the best accuracy of 90.88% in PSVMK of image size 75x75 for ABFI dataset, the optimized Gabor features recorded the best accuracy of 96.93% in GSVMK of image size 125x125 for ORL image dataset, the lowest error rate of 08.18% was obtained in LSVMK with image size of 150x150 for ORL image dataset
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