Automatic human body feature extraction and personal size measurement. (August 2018)

• Record Type: Journal Article
• Authors: Xiaohui, Tan; Xiaoyu, Peng; Liwen, Liu; Qing, Xia
• Is Part Of: Journal of visual languages & computing. Volume 47(2018)
• Page Start: 9
• Page End: 18
• Publication Date: 2018-08
• Subjects: Size measurement -- Feature points extraction -- Geodesic distance -- Heat kernel; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2018.05.002
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: It is a pervasive problem to automatically obtain the size of a human body without contacting for applications like virtual try-on. In this paper, we propose a novel approach to calculate human body size, such as width of shoulder, girths of bust, hips and waist. First, a depth camera as the 3D model acquisition device is used to get the 3D human body model. Then an automatic extraction method of focal features on 3D human body via random forest regression analysis of geodesic distances is used to extract the predefined feature points and lines. Finally, the individual human body size is calculated according to these feature points and lines. The scale-invariant heat kernel signature is exploited to serve as feature proximity. So our method is insensitive to postures and different shapes of 3D human body. These main advantages of our method lead to robust and accurate feature extraction and size measurement for 3D human bodies in various postures and shapes. The experiment results show that the average error of feature points extraction is 0.0617cm, the average errors of shoulder width and girth are 1.332 cm and 0.7635 cm, respectively. Overall, our algorithm has a better detection effect for 3D human body size, and it is stable with better robustness than existing methods.