Missing body measurements prediction in fashion industry: a comparative approach

Abstract The use of artificial intelligence to predict body dimensions rather than measuring them by stylists or 3D scanners permits to obtain easily all measurements of individual consumers and can consequently reduce costs of population survey campaigns. In this paper, we have compared several mod...
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Autor*in:	Meyer, Philippe [verfasserIn] Birregah, Babiga Beauseroy, Pierre Grall, Edith Lauxerrois, Audrey

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Artificial intelligence Machine learning Fashion and apparel industry Anthropometric measurement Sizing system

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Fashion and Textiles - Berlin : SpringerOpen, 2014, 10(2023), 1 vom: 05. Okt.
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:1 ; day:05 ; month:10

Links:	Volltext

DOI / URN:	10.1186/s40691-023-00357-5

Katalog-ID:	SPR053306910

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spelling	10.1186/s40691-023-00357-5 doi (DE-627)SPR053306910 (SPR)s40691-023-00357-5-e DE-627 ger DE-627 rakwb eng Meyer, Philippe verfasserin (orcid)0000-0002-0618-2947 aut Missing body measurements prediction in fashion industry: a comparative approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The use of artificial intelligence to predict body dimensions rather than measuring them by stylists or 3D scanners permits to obtain easily all measurements of individual consumers and can consequently reduce costs of population survey campaigns. In this paper, we have compared several models of machine learning to predict about 30 measurements used in fashion industry to construct clothes from 6 easy-to-measure body dimensions and demographic information. The four types of models we have studied are linear regressions, random forests, gradient boosting trees and support vector regressions. To construct and train them we have used anthropometric measurements of 9000 adult individuals of the French population collected by the French Institute of Textiles and Clothing (IFTH) during a national measurement campaign collected between 2003 and 2005. We have analyzed the model prediction performance in terms of individual and global predictions as well as the effect of the training dataset size and the importance of the input features. The linear and the support vector regressions have given the best results with respect to evaluation metrics, predicted distributions and have required less training data than tree-based models. It turns out that the weight and height have been the most important input features for the models considered while the hip girth has been the less important among the input measurements. Since the set of body dimensions used in fashion industry and the morphology depend on the gender, we have decided to treat men and women separately and to compare them. Artificial intelligence (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Fashion and apparel industry (dpeaa)DE-He213 Anthropometric measurement (dpeaa)DE-He213 Sizing system (dpeaa)DE-He213 Birregah, Babiga aut Beauseroy, Pierre aut Grall, Edith aut Lauxerrois, Audrey aut Enthalten in Fashion and Textiles Berlin : SpringerOpen, 2014 10(2023), 1 vom: 05. Okt. (DE-627)812496396 (DE-600)2802668-8 2198-0802 nnns volume:10 year:2023 number:1 day:05 month:10 https://dx.doi.org/10.1186/s40691-023-00357-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2023 1 05 10
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language	English
source	Enthalten in Fashion and Textiles 10(2023), 1 vom: 05. Okt. volume:10 year:2023 number:1 day:05 month:10
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topic_facet	Artificial intelligence Machine learning Fashion and apparel industry Anthropometric measurement Sizing system
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authorswithroles_txt_mv	Meyer, Philippe @@aut@@ Birregah, Babiga @@aut@@ Beauseroy, Pierre @@aut@@ Grall, Edith @@aut@@ Lauxerrois, Audrey @@aut@@
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author	Meyer, Philippe
spellingShingle	Meyer, Philippe misc Artificial intelligence misc Machine learning misc Fashion and apparel industry misc Anthropometric measurement misc Sizing system Missing body measurements prediction in fashion industry: a comparative approach
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topic_title	Missing body measurements prediction in fashion industry: a comparative approach Artificial intelligence (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Fashion and apparel industry (dpeaa)DE-He213 Anthropometric measurement (dpeaa)DE-He213 Sizing system (dpeaa)DE-He213
topic	misc Artificial intelligence misc Machine learning misc Fashion and apparel industry misc Anthropometric measurement misc Sizing system
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title	Missing body measurements prediction in fashion industry: a comparative approach
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title_sort	missing body measurements prediction in fashion industry: a comparative approach
title_auth	Missing body measurements prediction in fashion industry: a comparative approach
abstract	Abstract The use of artificial intelligence to predict body dimensions rather than measuring them by stylists or 3D scanners permits to obtain easily all measurements of individual consumers and can consequently reduce costs of population survey campaigns. In this paper, we have compared several models of machine learning to predict about 30 measurements used in fashion industry to construct clothes from 6 easy-to-measure body dimensions and demographic information. The four types of models we have studied are linear regressions, random forests, gradient boosting trees and support vector regressions. To construct and train them we have used anthropometric measurements of 9000 adult individuals of the French population collected by the French Institute of Textiles and Clothing (IFTH) during a national measurement campaign collected between 2003 and 2005. We have analyzed the model prediction performance in terms of individual and global predictions as well as the effect of the training dataset size and the importance of the input features. The linear and the support vector regressions have given the best results with respect to evaluation metrics, predicted distributions and have required less training data than tree-based models. It turns out that the weight and height have been the most important input features for the models considered while the hip girth has been the less important among the input measurements. Since the set of body dimensions used in fashion industry and the morphology depend on the gender, we have decided to treat men and women separately and to compare them. © The Author(s) 2023
abstractGer	Abstract The use of artificial intelligence to predict body dimensions rather than measuring them by stylists or 3D scanners permits to obtain easily all measurements of individual consumers and can consequently reduce costs of population survey campaigns. In this paper, we have compared several models of machine learning to predict about 30 measurements used in fashion industry to construct clothes from 6 easy-to-measure body dimensions and demographic information. The four types of models we have studied are linear regressions, random forests, gradient boosting trees and support vector regressions. To construct and train them we have used anthropometric measurements of 9000 adult individuals of the French population collected by the French Institute of Textiles and Clothing (IFTH) during a national measurement campaign collected between 2003 and 2005. We have analyzed the model prediction performance in terms of individual and global predictions as well as the effect of the training dataset size and the importance of the input features. The linear and the support vector regressions have given the best results with respect to evaluation metrics, predicted distributions and have required less training data than tree-based models. It turns out that the weight and height have been the most important input features for the models considered while the hip girth has been the less important among the input measurements. Since the set of body dimensions used in fashion industry and the morphology depend on the gender, we have decided to treat men and women separately and to compare them. © The Author(s) 2023
abstract_unstemmed	Abstract The use of artificial intelligence to predict body dimensions rather than measuring them by stylists or 3D scanners permits to obtain easily all measurements of individual consumers and can consequently reduce costs of population survey campaigns. In this paper, we have compared several models of machine learning to predict about 30 measurements used in fashion industry to construct clothes from 6 easy-to-measure body dimensions and demographic information. The four types of models we have studied are linear regressions, random forests, gradient boosting trees and support vector regressions. To construct and train them we have used anthropometric measurements of 9000 adult individuals of the French population collected by the French Institute of Textiles and Clothing (IFTH) during a national measurement campaign collected between 2003 and 2005. We have analyzed the model prediction performance in terms of individual and global predictions as well as the effect of the training dataset size and the importance of the input features. The linear and the support vector regressions have given the best results with respect to evaluation metrics, predicted distributions and have required less training data than tree-based models. It turns out that the weight and height have been the most important input features for the models considered while the hip girth has been the less important among the input measurements. Since the set of body dimensions used in fashion industry and the morphology depend on the gender, we have decided to treat men and women separately and to compare them. © The Author(s) 2023
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title_short	Missing body measurements prediction in fashion industry: a comparative approach
url	https://dx.doi.org/10.1186/s40691-023-00357-5
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author2	Birregah, Babiga Beauseroy, Pierre Grall, Edith Lauxerrois, Audrey
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Missing body measurements prediction in fashion industry: a comparative approach

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