Recognizing Human Behavior Using Universal Eigenspace

An efficient application of eigenspace technique to recognize human behaviors is described. The present paper investigates two types of recognition, i.e., recognition of an unknown human posture and a particular behavior by identifying human postures among several behaviors. A number of different posture sets from some selected behaviors create universal eigenspace and different sets of unknown postures are recognized from it. In contrast to the classical method, the paper proposes to employ some image processing of input images for better performance of the eigenspace technique instead of using just original images for human postures recognition. A new approach of producing eigenspace is described and the robustness of the method is effectively proved in the experiment.