Article | KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13 | Analyzing Kansei from Facial Expressions by CSRBF Mapping
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Title:
Analyzing Kansei from Facial Expressions by CSRBF Mapping
Author:
Luis Diago: InterLocus Inc, Japan/Meiji University, Japan Julian Romero: InterLocus Inc, Japan/Meiji University, Japan Junichi Shinoda: InterLocus Inc, Japan Ichiro Hagiwara: Meiji University, Japan
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Full text (pdf)
Year:
2014
Conference:
KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13
Issue:
100
Article no.:
073
Pages:
877-885
No. of pages:
9
Publication type:
Abstract and Fulltext
Published:
2014-06-11
ISBN:
978-91-7519-276-5
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


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This paper describes an application where a new Kansei/Affective Engineering (KAE) 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 fuzzy-quantized holographic neural network (FQHNN) 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 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; Linköping; Sweden; June 11-13

Author:
Luis Diago, Julian Romero, Junichi Shinoda, Ichiro Hagiwara
Title:
Analyzing Kansei from Facial Expressions by CSRBF Mapping
References:

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KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13

Author:
Luis Diago, Julian Romero, Junichi Shinoda, Ichiro Hagiwara
Title:
Analyzing Kansei from Facial Expressions by CSRBF Mapping
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