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A rule base generated from the lower and upper approximations of the fuzzy equivalence classes classifies the images through a voting process. Using genetic algorithm, the number of features required for classification is reduced by identifying the predictive image features. The margin of classification, which is a measure of the discriminative power of the classifier, is used to ensure the quality of classification process. The fitness function suggested assists in the feature selection process without compromising on the classification accuracy and margin. The proposed algorithm is tested using two hand posture and three face datasets. The algorithm provided good classification accuracy, at a less computational effort. The selection of relevant features further reduced the computational costs of both feature extraction and classification algorithms, which makes it suitable for real-time applications. The performance of the proposed algorithm is compared with that of support vector machines.<\/jats:p>","DOI":"10.1142\/s0219843610002180","type":"journal-article","created":{"date-parts":[[2010,11,15]],"date-time":"2010-11-15T09:15:30Z","timestamp":1289812530000},"page":"331-356","source":"Crossref","is-referenced-by-count":62,"title":["HAND POSTURE AND FACE RECOGNITION USING A FUZZY-ROUGH APPROACH"],"prefix":"10.1142","volume":"07","author":[{"given":"P. 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