Face Recognition is an area of emergent research, that offers great challenges, mainly in adverse conditions. This paper addresses face images with approximately 20% of face in partly occluded or not-well illuminated images as well as with use of disguise, scarf, sun glasses or masks. The presented techniques use three different eigenfeatures: eigeneyes, eigennose and eigenmouth. Even in these unfavorable situations, recognition rates achieved 87%. Images were extracted from The Yale Face Database.
Map Gauss filter is a linear adaptive filter commonly used to reduce speckle noise present in synthetic aperture radar images of remote sensing satellites. In this study was incorporating some modifications that allow us to maximize the signal-to-noise ratio at the same time almost total features of the image are preserved. To evalute the performance of the new filter, both original and filtered images were classified by a unsupervised technique known as Parallel Self-Organizing Map and the results of this classification were compared. The P-SOM is an algorithm with its own-organization mapping that is specific for parallel computing environment. As examples of applications, are presented the results of the classification for preprocessed original RADARSAT images using the Map gauss, Frost and Gamma filters.