Mining Inconsistent Emotion Recognition Results With the Multidimensional Model

The paper deals with the challenge of inconsistency in multichannel emotion recognition. The focus of the paper is to explore factors that might influence the inconsistency. The paper reports an experiment that used multi-camera facial expression analysis with multiple recognition systems. The data were analyzed using a multidimensional approach and data mining techniques. The study allowed us to explore camera location, occlusions and algorithm factors in the late fusion of emotion recognition results. We proposed to use a multidimensional data model for mining the various interdependencies between the factors of inconsistency. The study allowed the exploration of challenges in multichannel emotion recognition. It was achieved by comparing the consistency of obtained emotions and identification of rules determining conditions when the obtained emotions are consistent. However, the main novelty of the paper is the method of mining the inconsistencies. The study might be interesting both for researchers dealing with integration in emotion recognition, as well as for practitioners who use automatic emotion analysis software and expect to get valid results.