Face Recognition using Space, Scale and Orientation Domains

Abstract

This Project presents a robust face representation and recognition approach by exploring information jointly in image space, scale and orientation domains using LDA algorithm. Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. Gabor filters help to have Gabor images of different scale and rotation. Local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. Each pixel in image space is surrounded by pixels of different orientation, and different scale. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram with linear discriminant analysis techniques. The experimentation will be performed to test the recognition accuracy of face data set.