We propose a framework and a complete implementation of a translation and scale invariant image recognition system for natural indoor scenes. The system employs higher order autocorrelation features of scale space data which permit linear classification. An optimal linear classification method is presented, which is able to cope with a large number of classes represented by many, as well as very few samples. In the course of the analysis of our system, we examine which numerical methods for feature transformation and classification show sufficient stability to fulfill these demands. The implementation has been extensively tested. We present the results of our own application and several classification benchmarks. Image recognition Face recognition Scale invariancy Scale space Higher order autocorrelation Optimal linear classification 1. INTRODUCTION The task of visual recognition which was defined by Marr (1) with the question: "What objects are where in the environment?" is still ...

We propose a new kernel direct discriminant analysis (KDDA) algorithm in this paper. First, a recently advocated direct linear discriminant analysis (DLDA) algorithm is overviewed. Then the new KDDA algorithm is developed which can be considered as a kernel version of the DLDA algorithm. The design of the minimum distance classifier in the new kernel subspace is then discussed. The results of experiments on two well-known facial databases show the effectiveness of the proposed method in face recognition. The results of experiments also confirm that DLDA can be viewed as a special case of the proposed KDDA algorithm. 1.