In order to validate and gain a clear insight into the rules involved in the Kansei evaluation process; Procrustes analysis and Compactly-Supported Radial Basis Functions (CSRBF) are combined to generate new facial images. Procrustes analysis is used to find the minimal dissimilarity measure between two facial images with opposite classification (i.e. Iyashi and Non-Iyashi). CSRBFs are proposed for tuning of 17 facial parameters and mapping between facial images within opposite classes. The experiments with two subjects demonstrate that if only two from the five most important parameters of the face are changed then the Kansei evaluation can change to the opposite class. This paper shows that a continuous and efficient tuning of the design space can be achieved by introducing CSRBF mapping into the new KAE system
Keywords: Kansei evaluation; Iyashi expressions; neuro-fuzzy classifiers; radial basis functions
KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; LinkĂ¶ping; Sweden; June 11-13
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