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Comparative impression analysis between real and virtual human skins

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Published:05 August 2014Publication History

ABSTRACT

It is a common problem for many people to keep their skins fine, and there have been huge number of cosmetics products. We have developed of image recognition and geometric modeling technique for impression analysis of human skins [1]. The technique firstly extracts parameters of micro-geometry of human skins from real photographs, as shown in Figure 1. It then constructs similar micro-geometry of human skins by a polygon-based shape modeling technique with the parameters including radii of pores and directional distribution of furrows, extracted from the real photographs, as shown in Figure 2. This poster introduces the impression analysis results of the skin images generated by our skin simulation technique.

References

  1. F. Banba, et al., Micro-Geometric Skin Simulation for Face Impression Analysis, The Journal of Society for Art and Science, 13(1), 11--20, 2014.Google ScholarGoogle Scholar

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  1. Comparative impression analysis between real and virtual human skins

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        VINCI '14: Proceedings of the 7th International Symposium on Visual Information Communication and Interaction
        August 2014
        262 pages
        ISBN:9781450327657
        DOI:10.1145/2636240

        Copyright © 2014 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 5 August 2014

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        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        VINCI '14 Paper Acceptance Rate21of62submissions,34%Overall Acceptance Rate71of193submissions,37%
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