Abstract
In the image understanding process, observed physical values are transformed into high-level concepts. The knowledge used in this transformation is vague and context dependent. Therefore, it is difficult to capture and represent this knowledge in a computer based image understanding system. The same problem exists in an image creation system such as a computer graphics system, which transforms high-level concepts into numerical values. Conceptual Fuzzy Sets (CFS) implemented on a multi-layered bi-directional associative memories system, have been proposed to process such concepts and to derive a knowledge building method capable of addressing context depending issues. The basic mechanism of this method is inductive learning. On this paper we illustrate this method on the problem of modeling facial expressions. Computer simulation results show the suitability of this method for (1) recognition and (2) automatic generation of facial expressions.
Furthermore, with respect to (2) a learning method from linguistic instruction, based on CFS is proposed. Its effect is a better modeling of the user's mental image.
Computer simulation results showed that the learning is simpler than conventional linguistic learning, and can refine the knowledge to create desirable facial expressions.
