HAND POSTURE AND FACE RECOGNITION USING A FUZZY-ROUGH APPROACH

A fuzzy-rough multi cluster (FRMC) classifier for the recognition of hand postures and face is presented in this chapter. Features of the image are extracted using the computational model of the ventral stream of visual cortex. The recognition algorithm translates each quantitative value of the feature into fuzzy sets of linguistic terms using membership functions. The membership functions are generated by the fuzzy partitioning of the feature space into fuzzy equivalence classes, using the feature cluster centers generated by the subtractive clustering technique. 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 (GA), 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 algorithm is tested using two hand posture and three face datasets. The algorithm provides 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 algorithm is compared with that of Support Vector Machines.