General type-2 fuzzy edge detector applied on face recognition system using neural networks

Edge detection is an essential method used in the image processing systems and can be applied to image sets before the training phase in pattern recognition systems. An edge detector simplifies the analysis of the images; because, it reduces the dataset processed. In this paper we present the advantage to use a fuzzy edge detector method in a face recognition system. In the methodology, first the General type-2 fuzzy edge detector was applied over three image databases; secondly the recognition system was performed using monolithic neural network, and after that the mean recognition rate was obtained; finally the recognition rate is compared using different edge detectors, such as the Sobel operator, Type-1 and Interval Type-2 fuzzy edge detectors.