A Shape-based Sizing System for Facial Wearable Product Design

Wonsup Lee, Lyè Goto, Johan Molenbroek, Richard Goossens, Charlie Wang

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


A sizing system of a multiple-size product have been conventionally generated based on anthropometric size of a human body part. But a product which fit to a complex-shaped body part such as the face need to have a sizing system generated with consideration of body shape characteristics. This study applied template registration and machine learning clustering methods in order to make a sizing system which can consider variations of size and shape of the face. A hybrid approach using the bounded biharmonic weights (BBW) and non-rigid iterative closet point (ICP) registration methods was applied in this study to generate template-registered face images. Then, the Self-Organizing Map (SOM), a type of artificial neural network model for large-data clustering was used in order to cluster the template-registered face images into multiple shape categories. The proposed methods can be usefully applied in design of a facial wearable product such as face mask.
Original languageEnglish
Title of host publicationProceedings of the 5th International Digital Human Modeling Symposium
EditorsS. Wischniewski, D. Bonin, T. Alexander
Place of PublicationDortmund
PublisherFederal Institute for Occupational Safety and Health
Number of pages9
Publication statusPublished - 2017
EventThe 5th International Digital Human Modeling Symposium: 1st edition - Bonn, Germany
Duration: 26 Jun 201728 Jun 2017


ConferenceThe 5th International Digital Human Modeling Symposium


  • Sizing system
  • Template registration
  • Machine learning clustering
  • Self-Organizing Map
  • Facial wearable product


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