[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123896-en":3,"doc-seo-123896-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123896,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","The application of statistical and novel unsupervised machine learning methodology to forensic hair analysis","Hair colour serves as a key feature in forensic hair analysis and comparisons. The study uses 500 microscopic images of hair shafts and extracts colour model values based on RGB, CIE XYZ, and CIEL*a*b* spaces for each image. Statistical and unsupervised machine learning methods evaluate discriminating power among participants and assign hair to an individual. RGB provides the strongest discrimination, while CIE L*a*b* achieves complete discrimination with fewer participants. k-means is comparable; PCA/k-means and agglomerative clustering show weak image discrimination, indicating additional non-colour information.","Australian Journal of Forensic Sciences  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/tajf20)[www.tandfonline.com/journals/tajf20](homepage: www.tandfonline.com/journals/tajf20)  \nThe application of statistical and novel unsupervised machine learning methodology to forensic hair analysis  \nMelissa Airlie, James Robertson, Wanli Ma & Elizabeth Brooks  \nTo cite this article: Melissa Airlie, James Robertson, Wanli Ma & Elizabeth Brooks (22 Apr 2024): The application of statistical and novel unsupervised machine learning methodology to forensic hair analysis, Australian Journal of Forensic Sciences, DOI:  \n10. 1080/00450618 .2024.2343368  \nTo link to this article: [https://doi.org/10.1080/00450618.2024.2343368](https://doi.org/10.1080/00450618.2024.2343368)  \n© 2024 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n\n|  Published online: 22 Apr 2024. |\n| --- |\n|  Submit your article to this journal  |\n|  Article views: 80 |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tajf20](https://www.tandfonline.com/action/journalInformation?journalCode=tajf20)  \nThe application of statistical and novel unsupervised machine learning methodology to forensic hair analysis  \nMelissa Airlie a,b, James Robertson a, Wanli Ma a and Elizabeth Brooksa  \na Faculty of Science and Technology, University of Canberra, Bruce, ACT, Australia; bForensic Services Group, Queensland Police Service, Brisbane, QLD, Australia  \nABSTRACT  \nHair colour is a valuable feature in forensic hair analysis and hair comparisons. Five hundred microscopic images of hair shafts were taken and for each image and colour model (RGB, CIE XYZ and CIEL*a*b*) values for each image were determined. The discriminating power of the three colour model values using statistical and unsupervised machine learning methods was evaluated. Additionally, the discriminating power of the images of the same hair shafts using unsupervised machine learning methods was evaluated. All methods were compared to determine which method had the greatest discriminating power to best distinguished between participants and accurately assigned hair to an individual and assist in forensic hair comparisons. The RGB colour model demonstrated the highest discriminating power of the colour model values while the CIE L*a*b* model achieved complete discrimination with a reduced number of participants. The unsupervised k-means model yielded similar results. Unsupervised PCA/k-means and agglomerative clustering models demonstrated low discrimination power of the images, suggesting the existence of additional features within the data beyond colour. This research highlights the significance of the incorporation of colour values in forensic hair comparisons and for further exploration of the incorporation of other hair features, beyond colour values.  \nARTICLE HISTORY  \nReceived 31 December 2023 Accepted 10 April 2024  \nKEYWORDS  \nCanonical discriminant analysis; unsupervised machine learning; forensic hair analysis; colour; microscopic images  \n1. Introduction  \nSubjective assessments, inherent in forensic methodology, can create a vulnerability to forensics examination as these types of assessments are open to varied interpretation and susceptible to bias, opinions, and beliefs as opposed to facts and empirical evidence. Forensic scientists have acknowledged this vulnerability and the necessity for more objective feature comparison methodology 1. Forensic hair analysis requires the macroscopic and microscopic assessment of hair features for routine analysis and detailed morphological comparisons2,3. Hair features that can be objectively measured and assessed as discrete variables include hair length, shaft diameter, medullary index, scale  \nCONTACT Melissa Airlie  [airliemelissa@gmail.com](airliemelissa@gmail.com); [mel","cbCaidwgh0icYlAb","https://ap.wps.com/l/cbCaidwgh0icYlAb","pdf",2189132,1,15,"English","en",105,"# Abstract\n# Introduction\n# Article history\n# Keywords","[{\"question\":\"What data and colour representations were used in the study?\",\"answer\":\"The study analyzed 500 microscopic images of hair shafts and computed colour model values using RGB, CIE XYZ, and CIEL*a*b* for each image.\"},{\"question\":\"Which method and colour model performed best for discrimination?\",\"answer\":\"The RGB colour model values showed the highest discriminating power. The CIE L*a*b* model achieved complete discrimination with fewer participants.\"},{\"question\":\"Why did some unsupervised clustering approaches show low discrimination for images?\",\"answer\":\"Unsupervised PCA/k-means and agglomerative clustering demonstrated low discrimination power, suggesting that information beyond colour exists in the data.\"}]","The application of statistical and novel unsupervised machine learning methodology to forensic hair analysis | PDF",1785819128,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-application-of-statistical-and-novel-unsupervised-machine-learning-methodology-to-forensic-hair-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-application-of-statistical-and-novel-unsupervised-machine-learning-methodology-to-forensic-hair-analysis/123896/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and colour representations were used in the study?","Question",{"text":75,"@type":76},"The study analyzed 500 microscopic images of hair shafts and computed colour model values using RGB, CIE XYZ, and CIEL*a*b* for each image.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which method and colour model performed best for discrimination?",{"text":80,"@type":76},"The RGB colour model values showed the highest discriminating power. The CIE L*a*b* model achieved complete discrimination with fewer participants.",{"name":82,"@type":73,"acceptedAnswer":83},"Why did some unsupervised clustering approaches show low discrimination for images?",{"text":84,"@type":76},"Unsupervised PCA/k-means and agglomerative clustering demonstrated low discrimination power, suggesting that information beyond colour exists in the data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]