Cultural-based visual expression: emotional analysis of human face via Peking Opera Painted Faces (POPF)

Wang, Ding, Kang, Jinsheng, Qin, Sheng-feng and Birringer, Johannes (2016) Cultural-based visual expression: emotional analysis of human face via Peking Opera Painted Faces (POPF). Multimedia Tools and Applications, 75 (19). pp. 11865-11891. ISSN 1380-7501

[img]
Preview
Text (Article)
Qin.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (8MB) | Preview
Official URL: http://dx.doi.org/10.1007/s11042-015-2665-7

Abstract

Peking Opera as a branch of Chinese traditional cultures and arts has a very distinct colourful facial make-up for all actors in the stage performance. Such make-up is stylised in nonverbal symbolic semantics which all combined together to form the painted faces to describe and symbolise the background, the characteristic and the emotional status of specific roles. A study of Peking Opera Painted Faces (POPF) was taken as an example to see how information and meanings can be effectively expressed through the change of facial expressions based on the facial motion within natural and emotional aspects. The study found that POPF provides exaggerated features of facial motion through images, and the symbolic semantics of POPF provides a high-level expression of human facial information. The study has presented and proved a creative structure of information analysis and expression based on POPF to improve the understanding of human facial motion and emotion.

Item Type: Article
Uncontrolled Keywords: emotion, facial expression, facial motion, motion capture, POPF, visual information
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Users 6424 not found.
Date Deposited: 01 Jun 2015 10:02
Last Modified: 01 Aug 2021 00:36
URI: http://nrl.northumbria.ac.uk/id/eprint/22719

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics