Design efficient technologies for context image analysis in dialog HCI using self-configuring novelty search genetic algorithm

The efficiency of HCI systems can be sufficiently improved by the analysis of additional contextual information about the human user and interaction process. The processing of visual context provides HCI with such information as user identification, age, gender, emotion recognition and others. In this work, an approach to adaptive model building for image classification is presented. The novelty search based upon the multi-objective genetic algorithm is used to stochastically design a variety of independent technologies, which provide different image analysis strategies. Finally, the ensemble based decision is built adaptively for the given image analysis problem.