抽象的な

Classification of Face Image Surface Information Features Based on Linear Discernment Analysis

Arash Kalami, and Tohid Sedghi

A head position classification system is a nonlinear dynamical system whose output has sensitive dependence on initial head Rotations. Head rotation feature extraction theory is the analysis of the behavior of such systems. As such, feature extraction theory is not really a theory of head rotation, but is more concerned with understanding the complex behavior of nonlinear classification systems. We will introduce briefly the study of such systems, and in particular, will be interested in determining under what head Rotations such a system becomes head position classification. A class of face image signals will be introduced. We will investigate one signal in this class, and apply the lateral information function to see whether they are of practical use in Classification of face Image Surface Information.