Classification of female body shape based on two-dimensional image and computer vision technology

Abstract

Traditional body classification methods are usually based on three-dimensional human body data. With the development of computer vision technology, two-dimensional (2D) anthropometry technology has garnered a great deal of research attention in the field of anthropometry. This paper presents a body shape classification and discrimination method using 2D images based on computer vision technology. The research included three main parts. (1) Index extraction of body shape classification based on computer vision. The orthogonal 2D human body image information of 362 young female samples was extracted. After normalizing the body height, three body shape classification indexes were separated: the body height pixel value ( H), the feature of the projected unit area ( ρ), and the feature of the projected area ratio of the front and side of the human body ( F). (2) Two-dimensional human body shape classification based on the two-step cluster model. The optimal classification number was determined, and the characteristics of each type of body shape were analyzed. (3) Automatic discrimination of the 2D human body shape based on the Bayesian algorithm. The correct rate of recognition was 94.8%. The results indicate that the body shape classification method based on computer vision technology and the selection of the proposed classification indexes are effective, and the accuracy of body shape recognition is high. In this paper, the classification of human body shape based on 2D digital images was realized, and this method can be applied to 2D anthropometry and other related fields.