Young women's body shape classification and recognition based on height and weight

doi:10.19398/j.att.202107031

Advanced Textile Technology ›› 2022, Vol. 30 ›› Issue (4): 200-206.DOI: 10.19398/j.att.202107031

• Apparel Engineering • Previous Articles Next Articles

Young women's body shape classification and recognition based on height and weight

JIN Shouning^a, XIA Yuanping^a, ZHANG Beibei^a, GU Bingfei^a,b,c

a. School of Fashion Design & Engineering;b. Zhejiang Provincial Research Center of Clothing Engineering Technology;c. Key Laboratory of Silk Culture Heritage and Products Design Digital Technology, Ministry of Culture and Tourism,P. R. China, Zhejiang Sci-Tech University, Hangzhou 310018, China

Received:2021-07-22 Online:2022-07-10 Published:2022-08-25

基于身高体重的青年女性躯干形态分类及识别

靳守宁^a, 夏圆平^a, 张贝贝^a, 顾冰菲^a,b,c

浙江理工大学,a.服装学院;b.浙江省服装工程技术研究中心;c.丝绸文化传承与产品设计数字化技术文化和旅游部重点实验室,杭州 310018

作者简介:靳守宁(1995-),女,河南周口人,硕士研究生,主要从事服装数字化技术方面的研究。
基金资助:
国家自然科学基金项目(61702461,61702460);浙江理工大学科研业务费专项资金资助项目(2020Q051);中国纺织工业联合会科技指导性项目(2018079);2020年“纺织之光”应用基础研究项目(J202007);浙江理工大学服装服饰文化创新团队(11310031282006)

Abstract

Abstract: For the purpose of quick and convenient recognition of young women's body shape, the body shape parameters of 304 young women aged 18-25 were obtained, including parameters of height, weight and girth. The body shapes of young women were divided into three categories (i.e., O fat body, H Well-balanced body and X thin body), and the discriminant rules of each type were summarized. Besides, a BP neural network prediction model based on height and weight was established to perform the size prediction of bust, waist and hip circumference. The results showed that according to the classification rules of three body-shape types, 88% of the samples predicted based on height and weight were correctly classified, proving that the method of body type recognition based on BP neural network prediction model in this study is feasible, and it can provide technical reference and theoretical basis for generating personalized patterns.

Key words: shape classification, BP neural network, girth prediction, body type recognition, young women

摘要： 为了快速便捷地识别青年女性躯干形态,获取了304名年龄在18～25周岁青年女性的躯干形态参数,包括身高、体重及围度相关参数,将女青年躯干形态分为3类(“O胖体”“H匀称体”“X瘦体”),并归纳出每类体型的判别规则;同时建立了基于身高、体重的BP神经网络预测模型,实现了胸围、腰围和臀围的尺寸预测。结果表明:依据3类青年女性躯干形态的分类规则,88%基于身高、体重预测的样本都被正确分类,证明本文中基于BP神经网络预测模型进行体型识别的方法可行,可为个性化样板的生成提供技术参考和理论依据。

关键词: 形态分类, BP神经网络, 围度预测, 体型识别, 青年女性

CLC Number:

TS941.17

JIN Shouning, XIA Yuanping, ZHANG Beibei, GU Bingfei. Young women's body shape classification and recognition based on height and weight[J]. Advanced Textile Technology, 2022, 30(4): 200-206.

靳守宁, 夏圆平, 张贝贝, 顾冰菲. 基于身高体重的青年女性躯干形态分类及识别[J]. 现代纺织技术, 2022, 30(4): 200-206.

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Young women's body shape classification and recognition based on height and weight

基于身高体重的青年女性躯干形态分类及识别

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