N-dimension closeness measurements used in dynamic pattern recognitions
Chia-Lun John Hu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
When a standard ND (N-dimension) curve is compared to a test ND curve, the Euclidean distance between these two curves is defined as the root-mean-square sum of the distances between each (test) point in the test curve and the point on the standard curve closest to that test point. This number of root-mean-square sum is called the closeness CLS of the test curve to the standard curve in the ND state space where each point in the space may represent a snap shot of a certain moving or time-varying object. This CLS number can be used to identify accurately, a test moving object against several standard moving objects stored in the memory of this dynamic pattern recognition system, according to the particular way each standard object moves. In this paper, we use the face recognition scheme as a particular example that leads to the general analysis and design procedure.