Face Expression Recognition through Broken Symmetries

Security systems, criminology, physical access control and man-machine interactions are examples of applications where recognition of human faces may be crucial. In the present paper a new signature, based on a measure of axial symmetry called DST, is proposed as a significant feature to analyze facial expressions. The measure of symmetry is an elaborate difference between the internal and external symmetry kernels of an object. The idea here is to use the evolution of the symmetry measure of a face over an ordered set of its sub-images. We claim that different evolutionary trends will represent different face expressions. The proposed signature has been tested on several face databases (psychological image collection at stirling and jaffe). Experimental results indicate that the proposed signature characterizes normal from happy expression with a good accuracy.