Performance Evaluation Of Four Distance Classifiers In Ant Colony Optimization-based Gabor Features For Facial Recognition

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In face recognition system, several techniques have been proposed for extraction of facial features such as Local Binary Pattern, Gabor-filters, Elastic Bunch Graph Matching, Principal Component Analysis and Hidden Markov Models. Gabor-filters, among other feature extraction techniques, remain a powerful and useful tool in image processing. Its optimal functionality as feature extraction in face recognition is traceable to its biological importance and computational properties. In spite of all the distinctive characteristics of Gabor-filters, this technique suffers high feature dimensionality. This study therefore aimed at reducing the high dimensionality of Gabor features. The objectives were to: (i) extract facial features using Gabor-filters; (ii) optimize the Gabor features extracted with Ant Colony Optimization (ACO); (iii) perform facial image matching with the use of some selected distance classifiers; Chebysev, City-block, Mahalanobis and Euclidean; and (iv) evaluate the performance based on classification accuracy, classification time, sensitivity, specificity and error rate. rnThe facial features was extracted using Gabor-filters with 5 scales and 8 orientations, then the extracted features were optimized by applying ACO on the Gabor features to obtain the optimal features. The optimized features was passed into selected distance classifiers. The performance evaluation of the proposed system was done using two face image datasets; Locally Acquired Face Image Database (LAFI) and Olivetti Research Laboratory Database (ORL). rnThe findings of the study were that: rn(i) gabor feature vectors were obtained for face image representation;rn(ii) optimal features with relevant and discriminant information were produced;rn(iii) the optimized features performed efficiently with some selected distance classifiers; rn(iv) the best classification accuracy of 97.14% was obtained in Mahanolobis of image size (150x150) for LAFI Database, while classification accuracy of 95.71% was achieved in Mahanolobis (150x150), Euclidean (150x150), City-block (75x75, 100x100, 150x150) for ORL database; rn(v) reduced classification time of 0.42507secs was obtained in Mahanolobis (125x125) for LAFI Database and 0.40422secs was obtained in Mahalanobis (125x125) for ORL Database; rn(vi) the best sensitivity of 98.33% was obtained in Mahanolobis image size of (150x150), City-block (125x125) for LAFI, while the same percentage of 98.33% in Euclidean (150x150) for ORL;rn(vii) the best specificity of 90% was achieved in Mahanolobis image size of (150x150), Euclidean (75x75, 100x100, 125x125), Chebysev (75x75, 125x125) and City-block (75x75); andrn(viii) the best error rate of 2.86% was achieved in Mahanolobis of image size 150x150 for LAFI Database and 4.29% was obtained in Mahanolobis (150x150), Euclidean (150x150) and City-block (75x75, 100x100) for ORL database. rnThe study concluded that the high dimensionality of Gabor features was well reduced and optimized by Ant Colony Optimization Algorithm. The performance of optimized Gabor features with the selected distance classifiers recorded better experimental results. Thus, the study recommended ACO as an effective feature optimization method for Gabor-features based face recognition system.

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Performance Evaluation Of Four Distance Classifiers In Ant Colony Optimization-based Gabor Features For Facial Recognition

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