Face recognition for homeland security: a computational intelligence approach

By utilizing morphological shared-weight neural networks (MSNN) that have been trained for face recognition, common access restriction points can be enhanced to identify particular individuals of interest. A trained MSNN is a computational intelligence structure that learns representation of a specific face that encodes in its connection weights the feature extraction and classification abilities needed to identify an instance of that face. It has been shown effective in analyzing images that contain the target in a group of faces, even with the target face at varying orientations and lighting, as well as occluded target faces. The experiments presented here show the possible application of the MSNN to perform watch-list scanning of faces as individuals pass through access screening areas.