Complementary computing for visual tasks: Meshing computer vision with human visual processing

We explore the opportunity to harness electroencephalograph (EEG) signals generated during human visual processing to enhance computer vision systems. We review the challenging task of categorizing objects, such as faces, in images and then describe methods that can be used to combine the complementary competencies of human and machine computation to achieve improved recognition performance. We present the results of several experiments where brain signals, recorded from people examining images, are used to enhance the performance of vision systems on categorization tasks. We find that significant gains in classification accuracy can be achieved with the human-aided vision systems.