Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features
Objective The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performa...
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| Veröffentlicht in: | International journal of cosmetic science 2021-08, Vol.43 (4), p.405-418 |
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| creator | Daniels, Gabriela Tamburic, Slobodanka Benini, Sergio Randall, Jane Sanderson, Tracey Savardi, Mattia |
| description | Objective
The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms.
Methods
Machine learning was applied to a data set of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments) and t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2) and an online front image‐based survey (paired comparison of t0 vs t1, t1 vs t2, t0 vs t2) were conducted to compare human assessment with that of the algorithms.
Results
The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair.
Conclusions
This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis and highlighted the challenges imposed by the presentation mode.
Résumé
Objectif
Le premier objectif de cette étude était d'appliquer des techniques de vision par ordinateur et d'apprentissage automatique pour quantifier les effets des traitements capillaires sur l'organisation des cheve |
| doi_str_mv | 10.1111/ics.12706 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2512735376</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2512735376</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3536-7ee4bc1c954daeb1aaf829b0506362fb115836a0c846d8608daa208c4b87e3043</originalsourceid><addsrcrecordid>eNp1kb1OwzAURi0EoqUw8ALIEgsMoXbsuA5bVfFTqRIDMEeOc9O6pEmxE1A2HoFn5Elwm8KAhAd7OT7-fD-ETim5on4NjXZXNBwRsYf6lAsZhDzm-6hPKA-DiAvWQ0fOLQkhPJbsEPUYk1wyIfroZWxrkxttVIGnZQ1FYeZQasCmxAtlLLbgQFm9uMZjvLZVlX99fG43XXlsXWNXN1mLqxLDmyoaVZty3t1UzsEqLVqcg6ob7zlGB7kqHJzszgF6vr15mtwHs4e76WQ8CzSLmAhGADzVVMcRzxSkVKlchnFKIiKYCPOU0shnV0RLLjIpiMyUConUPJUjYISzAbrovD7vawOuTlbGaf81VULVuCSM_LD8UyPh0fM_6LJqbOnTeSqSNJaUbKjLjtK2cs5CnqytWSnbJpQkmwYS30CybcCzZztjk64g-yV_Ru6BYQe8mwLa_03JdPLYKb8BxK6Rqw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2558198106</pqid></control><display><type>article</type><title>Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><creator>Daniels, Gabriela ; Tamburic, Slobodanka ; Benini, Sergio ; Randall, Jane ; Sanderson, Tracey ; Savardi, Mattia</creator><creatorcontrib>Daniels, Gabriela ; Tamburic, Slobodanka ; Benini, Sergio ; Randall, Jane ; Sanderson, Tracey ; Savardi, Mattia</creatorcontrib><description>Objective
The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms.
Methods
Machine learning was applied to a data set of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments) and t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2) and an online front image‐based survey (paired comparison of t0 vs t1, t1 vs t2, t0 vs t2) were conducted to compare human assessment with that of the algorithms.
Results
The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair.
Conclusions
This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis and highlighted the challenges imposed by the presentation mode.
Résumé
Objectif
Le premier objectif de cette étude était d'appliquer des techniques de vision par ordinateur et d'apprentissage automatique pour quantifier les effets des traitements capillaires sur l'organisation des cheveux et pour identifier précisément si des cheveux d’origine inconnue ont été traités ou non. Le deuxième objectif était d'explorer et de comparer les performances obtenues par évaluation humaine avec celles obtenues à partir d'algorithmes d'intelligence artificielle (IA).
Méthodes
L'apprentissage automatique a été appliqué à un ensemble de données d'images de cheveux (vierges et décolorés), à la fois non traités et traités avec une association de shampooing et après shampooing visant à augmenter le volume des cheveux tout en améliorant l'alignement des fibres capillaires et en réduisant les frisottis. La quantification automatique des caractéristiques suivantes de l'image capillaire a été réalisée : volumes capillaires locaux et globaux et alignement des cheveux. Ces caractéristiques ont été évaluées à trois moments : t0 (pas de traitement), t1 (deux traitements), t2 (trois traitements). Des tests de classification ont été appliqués pour tester la précision de l'apprentissage automatique. Un test sensoriel (comparaison par paire de t0 vs t2) et une enquête en ligne basée sur l'image frontale (comparaison par paire de t0 vs t1, t1 vs t2, t0 vs t2) ont été menés pour comparer l'évaluation humaine avec celle des algorithmes.
Résultats
L'analyse automatique des images a identifié des changements dans le volume et l'alignement des cheveux qui ont permis la validation des tests de classification, en particulier lorsque les images de cheveux ont été rassemblés en groupes non traités et traités. L'évaluation humaine des cheveux présentés par paires a confirmé l'analyse automatique des images. L'évaluation des images pour les cheveux vierges et décolorés n'était que partiellement en accord avec l'analyse du sous‐ensemble d'images utilisées dans l'enquête en ligne. Une hypothèse est que les traitements ont quelque peu changé la forme de la chevelure, l'effet étant plus prononcé avec les cheveux décolorés. Cela a rendu l'évaluation humaine des images plates plus difficile que lorsqu'elles sont visualisées directement en 3D. Dans l'ensemble, les cheveux décolorés ont présenté des effets de plus grande ampleur que les cheveux vierges.
Conclusion
Cette étude a illustré la capacité de l'intelligence artificielle pour la détection et la classification d'images capillaires, et pour l'analyse d'images des caractéristiques d'organisation des cheveux après traitements. Le bilan humain a partiellement confirmé l'analyse d'image et mis en évidence les enjeux posés par le mode de présentation.
Computer vision and machine learning techniques were successfully applied to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The performance of the artificial intelligence (AI) algorithms was compared with human assessment of tresses and tress images. Viewing hair tresses provided results more consistent with the AI analysis, whilst assessing hair images appeared more challenging for humans.</description><identifier>ISSN: 0142-5463</identifier><identifier>EISSN: 1468-2494</identifier><identifier>DOI: 10.1111/ics.12706</identifier><identifier>PMID: 33848366</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Algorithms ; Alignment ; Artificial Intelligence ; Assembly ; bleached hair ; Bleaching ; Classification ; Computer vision ; Fibers ; Hair ; Hair - chemistry ; Hair conditioners ; hair detection ; hair segmentation ; Human performance ; Humans ; Image analysis ; Image classification ; Image detection ; Image processing ; Learning algorithms ; Machine learning ; Polls & surveys ; Proof of Concept Study ; sensory assessment ; Shampoos ; virgin hair</subject><ispartof>International journal of cosmetic science, 2021-08, Vol.43 (4), p.405-418</ispartof><rights>2021 Society of Cosmetic Scientists and the Société Française de Cosmétologie</rights><rights>2021 Society of Cosmetic Scientists and the Société Française de Cosmétologie.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3536-7ee4bc1c954daeb1aaf829b0506362fb115836a0c846d8608daa208c4b87e3043</citedby><cites>FETCH-LOGICAL-c3536-7ee4bc1c954daeb1aaf829b0506362fb115836a0c846d8608daa208c4b87e3043</cites><orcidid>0000-0002-1754-5184</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fics.12706$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fics.12706$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33848366$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Daniels, Gabriela</creatorcontrib><creatorcontrib>Tamburic, Slobodanka</creatorcontrib><creatorcontrib>Benini, Sergio</creatorcontrib><creatorcontrib>Randall, Jane</creatorcontrib><creatorcontrib>Sanderson, Tracey</creatorcontrib><creatorcontrib>Savardi, Mattia</creatorcontrib><title>Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features</title><title>International journal of cosmetic science</title><addtitle>Int J Cosmet Sci</addtitle><description>Objective
The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms.
Methods
Machine learning was applied to a data set of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments) and t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2) and an online front image‐based survey (paired comparison of t0 vs t1, t1 vs t2, t0 vs t2) were conducted to compare human assessment with that of the algorithms.
Results
The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair.
Conclusions
This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis and highlighted the challenges imposed by the presentation mode.
Résumé
Objectif
Le premier objectif de cette étude était d'appliquer des techniques de vision par ordinateur et d'apprentissage automatique pour quantifier les effets des traitements capillaires sur l'organisation des cheveux et pour identifier précisément si des cheveux d’origine inconnue ont été traités ou non. Le deuxième objectif était d'explorer et de comparer les performances obtenues par évaluation humaine avec celles obtenues à partir d'algorithmes d'intelligence artificielle (IA).
Méthodes
L'apprentissage automatique a été appliqué à un ensemble de données d'images de cheveux (vierges et décolorés), à la fois non traités et traités avec une association de shampooing et après shampooing visant à augmenter le volume des cheveux tout en améliorant l'alignement des fibres capillaires et en réduisant les frisottis. La quantification automatique des caractéristiques suivantes de l'image capillaire a été réalisée : volumes capillaires locaux et globaux et alignement des cheveux. Ces caractéristiques ont été évaluées à trois moments : t0 (pas de traitement), t1 (deux traitements), t2 (trois traitements). Des tests de classification ont été appliqués pour tester la précision de l'apprentissage automatique. Un test sensoriel (comparaison par paire de t0 vs t2) et une enquête en ligne basée sur l'image frontale (comparaison par paire de t0 vs t1, t1 vs t2, t0 vs t2) ont été menés pour comparer l'évaluation humaine avec celle des algorithmes.
Résultats
L'analyse automatique des images a identifié des changements dans le volume et l'alignement des cheveux qui ont permis la validation des tests de classification, en particulier lorsque les images de cheveux ont été rassemblés en groupes non traités et traités. L'évaluation humaine des cheveux présentés par paires a confirmé l'analyse automatique des images. L'évaluation des images pour les cheveux vierges et décolorés n'était que partiellement en accord avec l'analyse du sous‐ensemble d'images utilisées dans l'enquête en ligne. Une hypothèse est que les traitements ont quelque peu changé la forme de la chevelure, l'effet étant plus prononcé avec les cheveux décolorés. Cela a rendu l'évaluation humaine des images plates plus difficile que lorsqu'elles sont visualisées directement en 3D. Dans l'ensemble, les cheveux décolorés ont présenté des effets de plus grande ampleur que les cheveux vierges.
Conclusion
Cette étude a illustré la capacité de l'intelligence artificielle pour la détection et la classification d'images capillaires, et pour l'analyse d'images des caractéristiques d'organisation des cheveux après traitements. Le bilan humain a partiellement confirmé l'analyse d'image et mis en évidence les enjeux posés par le mode de présentation.
Computer vision and machine learning techniques were successfully applied to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The performance of the artificial intelligence (AI) algorithms was compared with human assessment of tresses and tress images. Viewing hair tresses provided results more consistent with the AI analysis, whilst assessing hair images appeared more challenging for humans.</description><subject>Algorithms</subject><subject>Alignment</subject><subject>Artificial Intelligence</subject><subject>Assembly</subject><subject>bleached hair</subject><subject>Bleaching</subject><subject>Classification</subject><subject>Computer vision</subject><subject>Fibers</subject><subject>Hair</subject><subject>Hair - chemistry</subject><subject>Hair conditioners</subject><subject>hair detection</subject><subject>hair segmentation</subject><subject>Human performance</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image classification</subject><subject>Image detection</subject><subject>Image processing</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Polls & surveys</subject><subject>Proof of Concept Study</subject><subject>sensory assessment</subject><subject>Shampoos</subject><subject>virgin hair</subject><issn>0142-5463</issn><issn>1468-2494</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp1kb1OwzAURi0EoqUw8ALIEgsMoXbsuA5bVfFTqRIDMEeOc9O6pEmxE1A2HoFn5Elwm8KAhAd7OT7-fD-ETim5on4NjXZXNBwRsYf6lAsZhDzm-6hPKA-DiAvWQ0fOLQkhPJbsEPUYk1wyIfroZWxrkxttVIGnZQ1FYeZQasCmxAtlLLbgQFm9uMZjvLZVlX99fG43XXlsXWNXN1mLqxLDmyoaVZty3t1UzsEqLVqcg6ob7zlGB7kqHJzszgF6vr15mtwHs4e76WQ8CzSLmAhGADzVVMcRzxSkVKlchnFKIiKYCPOU0shnV0RLLjIpiMyUConUPJUjYISzAbrovD7vawOuTlbGaf81VULVuCSM_LD8UyPh0fM_6LJqbOnTeSqSNJaUbKjLjtK2cs5CnqytWSnbJpQkmwYS30CybcCzZztjk64g-yV_Ru6BYQe8mwLa_03JdPLYKb8BxK6Rqw</recordid><startdate>202108</startdate><enddate>202108</enddate><creator>Daniels, Gabriela</creator><creator>Tamburic, Slobodanka</creator><creator>Benini, Sergio</creator><creator>Randall, Jane</creator><creator>Sanderson, Tracey</creator><creator>Savardi, Mattia</creator><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>WIN</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-1754-5184</orcidid></search><sort><creationdate>202108</creationdate><title>Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features</title><author>Daniels, Gabriela ; Tamburic, Slobodanka ; Benini, Sergio ; Randall, Jane ; Sanderson, Tracey ; Savardi, Mattia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3536-7ee4bc1c954daeb1aaf829b0506362fb115836a0c846d8608daa208c4b87e3043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Alignment</topic><topic>Artificial Intelligence</topic><topic>Assembly</topic><topic>bleached hair</topic><topic>Bleaching</topic><topic>Classification</topic><topic>Computer vision</topic><topic>Fibers</topic><topic>Hair</topic><topic>Hair - chemistry</topic><topic>Hair conditioners</topic><topic>hair detection</topic><topic>hair segmentation</topic><topic>Human performance</topic><topic>Humans</topic><topic>Image analysis</topic><topic>Image classification</topic><topic>Image detection</topic><topic>Image processing</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Polls & surveys</topic><topic>Proof of Concept Study</topic><topic>sensory assessment</topic><topic>Shampoos</topic><topic>virgin hair</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Daniels, Gabriela</creatorcontrib><creatorcontrib>Tamburic, Slobodanka</creatorcontrib><creatorcontrib>Benini, Sergio</creatorcontrib><creatorcontrib>Randall, Jane</creatorcontrib><creatorcontrib>Sanderson, Tracey</creatorcontrib><creatorcontrib>Savardi, Mattia</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Free Content</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>International journal of cosmetic science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Daniels, Gabriela</au><au>Tamburic, Slobodanka</au><au>Benini, Sergio</au><au>Randall, Jane</au><au>Sanderson, Tracey</au><au>Savardi, Mattia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features</atitle><jtitle>International journal of cosmetic science</jtitle><addtitle>Int J Cosmet Sci</addtitle><date>2021-08</date><risdate>2021</risdate><volume>43</volume><issue>4</issue><spage>405</spage><epage>418</epage><pages>405-418</pages><issn>0142-5463</issn><eissn>1468-2494</eissn><abstract>Objective
The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms.
Methods
Machine learning was applied to a data set of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments) and t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2) and an online front image‐based survey (paired comparison of t0 vs t1, t1 vs t2, t0 vs t2) were conducted to compare human assessment with that of the algorithms.
Results
The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair.
Conclusions
This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis and highlighted the challenges imposed by the presentation mode.
Résumé
Objectif
Le premier objectif de cette étude était d'appliquer des techniques de vision par ordinateur et d'apprentissage automatique pour quantifier les effets des traitements capillaires sur l'organisation des cheveux et pour identifier précisément si des cheveux d’origine inconnue ont été traités ou non. Le deuxième objectif était d'explorer et de comparer les performances obtenues par évaluation humaine avec celles obtenues à partir d'algorithmes d'intelligence artificielle (IA).
Méthodes
L'apprentissage automatique a été appliqué à un ensemble de données d'images de cheveux (vierges et décolorés), à la fois non traités et traités avec une association de shampooing et après shampooing visant à augmenter le volume des cheveux tout en améliorant l'alignement des fibres capillaires et en réduisant les frisottis. La quantification automatique des caractéristiques suivantes de l'image capillaire a été réalisée : volumes capillaires locaux et globaux et alignement des cheveux. Ces caractéristiques ont été évaluées à trois moments : t0 (pas de traitement), t1 (deux traitements), t2 (trois traitements). Des tests de classification ont été appliqués pour tester la précision de l'apprentissage automatique. Un test sensoriel (comparaison par paire de t0 vs t2) et une enquête en ligne basée sur l'image frontale (comparaison par paire de t0 vs t1, t1 vs t2, t0 vs t2) ont été menés pour comparer l'évaluation humaine avec celle des algorithmes.
Résultats
L'analyse automatique des images a identifié des changements dans le volume et l'alignement des cheveux qui ont permis la validation des tests de classification, en particulier lorsque les images de cheveux ont été rassemblés en groupes non traités et traités. L'évaluation humaine des cheveux présentés par paires a confirmé l'analyse automatique des images. L'évaluation des images pour les cheveux vierges et décolorés n'était que partiellement en accord avec l'analyse du sous‐ensemble d'images utilisées dans l'enquête en ligne. Une hypothèse est que les traitements ont quelque peu changé la forme de la chevelure, l'effet étant plus prononcé avec les cheveux décolorés. Cela a rendu l'évaluation humaine des images plates plus difficile que lorsqu'elles sont visualisées directement en 3D. Dans l'ensemble, les cheveux décolorés ont présenté des effets de plus grande ampleur que les cheveux vierges.
Conclusion
Cette étude a illustré la capacité de l'intelligence artificielle pour la détection et la classification d'images capillaires, et pour l'analyse d'images des caractéristiques d'organisation des cheveux après traitements. Le bilan humain a partiellement confirmé l'analyse d'image et mis en évidence les enjeux posés par le mode de présentation.
Computer vision and machine learning techniques were successfully applied to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The performance of the artificial intelligence (AI) algorithms was compared with human assessment of tresses and tress images. Viewing hair tresses provided results more consistent with the AI analysis, whilst assessing hair images appeared more challenging for humans.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>33848366</pmid><doi>10.1111/ics.12706</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-1754-5184</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0142-5463 |
| ispartof | International journal of cosmetic science, 2021-08, Vol.43 (4), p.405-418 |
| issn | 0142-5463 1468-2494 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2512735376 |
| source | MEDLINE; Access via Wiley Online Library |
| subjects | Algorithms Alignment Artificial Intelligence Assembly bleached hair Bleaching Classification Computer vision Fibers Hair Hair - chemistry Hair conditioners hair detection hair segmentation Human performance Humans Image analysis Image classification Image detection Image processing Learning algorithms Machine learning Polls & surveys Proof of Concept Study sensory assessment Shampoos virgin hair |
| title | Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T16%3A59%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Artificial%20Intelligence%20in%20hair%20research:%20A%20proof%E2%80%90of%E2%80%90concept%20study%20on%20evaluating%20hair%20assembly%20features&rft.jtitle=International%20journal%20of%20cosmetic%20science&rft.au=Daniels,%20Gabriela&rft.date=2021-08&rft.volume=43&rft.issue=4&rft.spage=405&rft.epage=418&rft.pages=405-418&rft.issn=0142-5463&rft.eissn=1468-2494&rft_id=info:doi/10.1111/ics.12706&rft_dat=%3Cproquest_cross%3E2512735376%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2558198106&rft_id=info:pmid/33848366&rfr_iscdi=true |