Automatic Body Feature Extraction from Front and Side Images

Lingyan Jiang, Jian Yao, Baopu Li, Fei Fang, Qi Zhang, Max Q.-H. Meng

Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, P.R. China.
School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, P.R. China.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, P.R. China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P.R. China.

DOI: 10.4236/jsea.2012.512B019                

Abstract

Human body feature extraction based on 2D images provides an efficient method for many applications, e.g. non-contact body size measurements, constructing 3D human model and recognizing human actions. In this paper a systematic approach is proposed to detect feature points of human body automatically from its front and side images. Firstly, an efficient approach for silhouette and contour detection is used to represent the contour curves of a human body shape with Freeman’s 8-connected chain codes. The contour curves are considered as a number of segments connected together. Then, a series of feature points on human body are extracted based on the specified rules by measuring the differences between the directions of the segments. In total, 101 feature points with clearly geometric properties (that rather accurately reflect the bump or turning of the contours) are extracted automatically, including 27 points corresponding to the definitions of the landmarks about garment measurements. Finally, the proposed approach was tested on ten human subjects and the entire 101 feature points with specific geography geometrical characteristics were correctly extracted, indicating an effective and robust performance.

 

Keywords

Silhouette detection; Contour representation; Human feature point extraction

Conflicts of Interest

The authors declare no conflicts of interest.

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