Face Recognition in Complex Background: Developmental Network and Synapse Maintenance

Abstract

As an important sub-field in computer vision and pattern recognition, face recognition has important theoretical and practical application. It is a very complicated problem which is often affected by variations in illumination condition, head pose, facial emotions, glasses, beards, and so on. 108 face images of 27 learned objects (appearances) from complex backgrounds in BioID face database are efficiently recognized with the developmental network (DN) - a biologically inspired framework with the emergent representation. But the DN has no adaptive receptive field for an object with a curved contour. Leaked-in background pixels can lead to problems when different objects look similar. This paper introduces another biologically inspired mechanism - synapse maintenance to achieve the object recognition. Synapse maintenance can automatically decide which synapse should be removed, kept or partial removed, thus it can weaken the complex background, strengthen the face features, reduce the bad influence of the complex background on the face recognition. Experimental results show that DN with the synapse maintenance can effectively recognize faces with complicated backgrounds and the recognition rate is over 95%.