The Science of Pattern Recognition. Achievements and Perspectives

Automatic pattern recognition is usually considered as an engineering area which focusses on the development and evaluation of systems that imitate or assist humans in their ability of recognizing patterns. It may, however, also be considered as a science that studies the faculty of human beings (and possibly other biological systems) to discover, distinguish, characterize patterns in their environment and accordingly identify new observations. The engineering approach to pattern recognition is in this view an attempt to build systems that simulate this phenomenon. By doing that, scientific understanding is gained of what is needed in order to recognize patterns, in general. Like in any science understanding can be built from different, sometimes even opposite viewpoints. We will therefore introduce the main approaches to the science of pattern recognition as two dichotomies of complementary scenarios. They give rise to four different schools, roughly defined under the terms of expert systems, neural networks, structural pattern recognition and statistical pattern recognition. We will briefly describe what has been achieved by these schools, what is common and what is specific, which limitations are encountered and which perspectives arise for the future. Finally, we will focus on the challenges facing pattern recognition in the decennia to come. They mainly deal with weaker assumptions of the models to make the corresponding procedures for learning and recognition wider applicable. In addition, new formalisms need to be developed.