This paper describes an application where a new KAE （Kansei/Affective Engineering） system was applied to define the properties of the facial images perceived as Iyashi. Iyashi is a Japanese word used to describe a p...This paper describes an application where a new KAE （Kansei/Affective Engineering） system was applied to define the properties of the facial images perceived as Iyashi. Iyashi is a Japanese word used to describe a peculiar phenomenon that is mentally soothing, but is yet to be clearly defined. Instead of analyzing facial expressions of an individual to determine his emotional state, the proposed system introduces a FQHNN （fuzzy-quantized holographic neural network） to find the rules involved in the Kansei evaluation provided by the subjects about the limited dataset of 20 facial images. In order to validate and gain a clear insight into the rules involved in the Kansei evaluation process, Procrustes analysis and CSRBFs （compactly-supported radial basis functions） 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-lyashi）. 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.展开更多
文摘This paper describes an application where a new KAE （Kansei/Affective Engineering） system was applied to define the properties of the facial images perceived as Iyashi. Iyashi is a Japanese word used to describe a peculiar phenomenon that is mentally soothing, but is yet to be clearly defined. Instead of analyzing facial expressions of an individual to determine his emotional state, the proposed system introduces a FQHNN （fuzzy-quantized holographic neural network） to find the rules involved in the Kansei evaluation provided by the subjects about the limited dataset of 20 facial images. In order to validate and gain a clear insight into the rules involved in the Kansei evaluation process, Procrustes analysis and CSRBFs （compactly-supported radial basis functions） 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-lyashi）. 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.
