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A Soft-Computing Approach for Quantification of Personal Perceptions

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Advances in Affective and Pleasurable Design

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 483))

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Abstract

Soft-computing forms the basis of a considerable amount of machine learning techniques which deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. This paper describes an application developed to understand what means a picture (portrait) to be Iyashi. The neuro-fuzzy quantification allowed extracting a set of 35 rules that describe the meaning of the word Iyashi to hundreds of users. Facial expressions of the subjects and their brain signals during the evaluation of the images have been explored to validate the obtained rules. The developed system allows discovering the rules that describe the preferences of users while exploring the space of possible design parameters so that the system predictions match the preferences of users. Interactive genetic algorithms (IGAs) have been used for the implementation of a color recommendation system following customer’s preferences. The combination of color and geometric shapes is also explored.

Iyashi is a Japanese word used to describe a peculiar phenomenon that is mentally soothing, but is yet to be clearly defined.

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Notes

  1. 1.

    Available at https://www.bioid.com/About/BioID-Face-Database.

  2. 2.

    Available at https://github.com/subprotocol/genetic-js.

  3. 3.

    Available at https://github.com/cazala/synaptic.

  4. 4.

    Available at http://evanw.github.io/csg.js/.

References

  1. Diago, L., Kitaoka, T., Hagiwara, I., Kambayashi, T.: Neuro-fuzzy quantification of personal perceptions of facial images based on a limited data set. IEEE Trans. Neural Netw. 22(12), 2422–2434 (2011)

    Article  Google Scholar 

  2. Rodriguez, L., Diago, L., Hagiwara, I.: Interactive genetic algorithm with fitness modeling for the development of a color simulation system based on customer’s preference. Jpn. J. Ind. Appl. Math. 28(1), 27–42 (2011)

    Google Scholar 

  3. Reinecke, K., Yeh, T., Miratrix, L., Mardiko, R., Zhao, Y., Liu, J., Gajos, K.Z.: Predicting users’ first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness, In: Proceedings of the CHI 2013, April 27–May 2, 2013, Paris, France

    Google Scholar 

  4. Bradley, M., Lang, P.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Psychiat. 25(1), 49–59 (1994)

    Google Scholar 

  5. Lang, P., Bradley, M., Cuthbert, B.: International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual. University of Florida, Gainesville, FL, Technical Report A-8 (2008)

    Google Scholar 

  6. Mikels, J., Fredrickson, B., Larkin, G.R., Lindberg, C., Maglio, S., Reuter-Lorenz, P.: Emotional category data on images from the international affective picture system. Behav. Res. Methods 36(4), 626–630 (2005)

    Google Scholar 

  7. Russell, J., Barrett, L.: Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. J. Pers. Soc. Psychol. 76(5), 805–819 (1999)

    Google Scholar 

  8. Takahashi, K.: Remarks on emotion recognition from bio-potential signals. In: 2nd International Conference on Autonomous Robots and Agents, pp. 186–191 (2004)

    Google Scholar 

  9. Bos, D.O.: EEG-based Emotion Recognition: The Influence of Visual and Auditory Stimuli. Department of Computer Science, University of Twente, Netherlands (2006)

    Google Scholar 

  10. Partala, T., Jokiniemi, M., Surakka, V.: Pupillary responses to emotionally provocative stimuli. In: ETRA 00: Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, pp. 123–129, NY, USA (2000)

    Google Scholar 

  11. Diago, L.A., Romero, J., Shinoda, J., Hagiwara, I.: Analysis Kansei from facial expressions by CSRBF mapping. In: Proceedings of the International Conference on Kansei Engineering & Emotion Research, KEER 2014, Linkoping University, Sweden, June 11–13, 2014

    Google Scholar 

  12. Wendland, H.: Piecewise polynomial, positive defined and compactly supported radial functions of minimal degree. AICM 4, 389–396 (1995)

    MathSciNet  MATH  Google Scholar 

  13. Romero, J., Diago, L.A., Shinoda, J., Hagiwara, I.: Verification of models of personal perception of faces by closed-eye classifier using histogram correlation. In: Proceedings of the ASME (IDETC/CIE 2015), Boston, Massachusetts, 2–5 Aug 2015

    Google Scholar 

  14. Musha, T., Terasaki, Y., Haque, H.A., Ivanitsky, G.A.: Feature extraction from EEGs associated with emotions. Artif. Life Robot. 1, 15–19 (1997). (ch. 1)

    Article  Google Scholar 

  15. Murakami, K., Nakagawa, M.: Proceedings of the 2nd International Conference on Kansei Engineering & Affective Systems KEAS’08, pp. 51–56, Nagaoka, Japan, (2008)

    Google Scholar 

  16. Romero, J., Diago, L.A., Shinoda, J., Hagiwara, I.: Comparison of data reduction methods for the analysis of Iyashi expressions using brain signals. J. Adv. Simul. Sci. Eng. 2(2), 349–366 (2015)

    Article  Google Scholar 

  17. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Trans. Knowl. Data Eng. 17(16) (2005)

    Google Scholar 

  18. Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. In: Proceedings of the IEEE, pp. 1275–1296 (2001)

    Google Scholar 

  19. Ito, F., Hiroyasu, T., Miki, M., Yokouchi, H.: Discussion of offspring generation method for interactive genetic algorithms with consideration of multimodal preference. In: 7th International Conference, SEAL 2008, Lecture Notes in Computer Science, vol. 5361, pp. 349–359. Melbourne, Australia, 7–10 Dec 2008

    Google Scholar 

  20. Ochoa, A.A., Soto, M.R.: Partial evaluations of genetic algorithms. In: International Conference on Industrial and Engineering Applications, pp. 217–222. Atlanta, Georgia (1997)

    Google Scholar 

  21. Llora, X., Sastry, K., Golbert, D.E., Gupta, A., Lakshmi, L.: Combating user fatigue in IGAs: partial ordering, support vector machine and synthetic fitness. In: GECCO ’05: Proceedings of Genetic Evolutionary Computation Conference, pp. 1363–1370 (2005)

    Google Scholar 

  22. Schmidt, M.D., Lipson, H.: Coevolution of fitness predictors. IEEE Trans. Evol Comput. 12(6), 736–749 (2008)

    Article  Google Scholar 

  23. Yoo, S., Cho, S.: Partially evaluated genetic algorithm based on fuzzy c-means algorithms. PPSN VIII, LNCS 3242, 440–449 (2004)

    Google Scholar 

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Authors and Affiliations

  1. Interlocus Inc, TokyoTech YVP 305, 4259-3 Nagatuta-Cho, Midori-ku, Yokohama City, 226-8510, Japan

    Luis Diago, Julian Romero & Junichi Shinoda

  2. Meiji Institute for Advanced Study of Mathematical Sciences (MIMS), Meiji University, 4-21-1 Nakano, Nakano-ku, Tokyo, Japan

    Luis Diago, Julian Romero, Junichi Shinoda, Hiroe Abe & Ichiro Hagiwara

Authors
  1. Luis Diago
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  2. Julian Romero
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  3. Junichi Shinoda
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  4. Hiroe Abe
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  5. Ichiro Hagiwara
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Correspondence to Luis Diago .

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Editors and Affiliations

  1. School of Industrial Design, CarletonUniversity, Ottawa, Ontario, Canada

    WonJoon Chung

  2. Department of Industrial Design, University of Illinois at Urbana Champai, Champaign, IL, USA

    Cliff Sungsoo Shin

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Diago, L., Romero, J., Shinoda, J., Abe, H., Hagiwara, I. (2017). A Soft-Computing Approach for Quantification of Personal Perceptions. In: Chung, W., Shin, C. (eds) Advances in Affective and Pleasurable Design . Advances in Intelligent Systems and Computing, vol 483. Springer, Cham. https://doi.org/10.1007/978-3-319-41661-8_20

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  • DOI: https://doi.org/10.1007/978-3-319-41661-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41660-1

  • Online ISBN: 978-3-319-41661-8

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