General Type-2 fuzzy edge detectors applied to face recognition systems

Edge detection is an essential step used in image processing systems and can be applied to image sets before the training phase in pattern recognition systems to improve performance. An edge detector simplifies the analysis of the images; because, it reduces the data to be processed by highlighting the most important features. In this paper we show the advantage of using a fuzzy edge detector method in a face recognition system. In the proposed methodology, first the general type-2 fuzzy edge detector was applied over three image databases; secondly the recognition system was implemented using a monolithic neural network, and after that the mean recognition rate was obtained; finally the recognition rate is compared to other edge detectors, such as the Sobel operator, Type-1 and Interval Type-2 fuzzy edge detectors.