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A mobile application for personal colour analysis

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  • Hye Rim Hong
  • Young In Kim

Abstract

This study developed a mobile app of the personal colour analysis system, which determines the clothing colours that are harmonized to a user’s personal skin and hair colouring. The mobile application is easy and accurate for non-experts to use, and it employs photographs of a user’s skin and hair colours to automatically calculate and categorize personal colouring instead of using fabrics as an analysing material. The application applies virtual packets of fabric swatches to virtual tee shirts and overlays them on the user’s photographs to obtain a personal colour analysis. Through this process, the application reduces personal time and cost dramatically, and it allows users to analyse their personal colours repeatedly by themselves. This study is expected to establish a framework to advance research on clothing colours. The application is also expected to substantively assist consumers in choosing clothing colours.

Suggested Citation

  • Hye Rim Hong & Young In Kim, 2019. "A mobile application for personal colour analysis," Cogent Business & Management, Taylor & Francis Journals, vol. 6(1), pages 1576828-157, January.
    • Handle: RePEc:taf:oabmxx:v:6:y:2019:i:1:p:1576828
    DOI: 10.1080/23311975.2019.1576828
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    Cited by:

    1. Hyesun Hwang & Su‐Jung Nam, 2021. "Social media use and subjective well‐being among middle‐aged consumers in Korea: Mediation model of social capital moderated by disability," Journal of Consumer Affairs, Wiley Blackwell, vol. 55(4), pages 1352-1372, December.
    2. Sparacino, Daniele, 2019. "Funktionsweise und Möglichkeiten von Google Shopping mit einem Fokus auf Showcase Ads," Working Papers for Marketing & Management 42, Offenburg University, Department of Media and Information.
    3. Juliza Mohamad Arif & Mohd Faizal Ab Razak & Suryanti Awang & Sharfah Ratibah Tuan Mat & Nor Syahidatul Nadiah Ismail & Ahmad Firdaus, 2021. "A static analysis approach for Android permission-based malware detection systems," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-23, September.

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