[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117042-en":3,"doc-seo-117042-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117042,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Cheiloscopic Characteristics detection and Pattern Classification by Machine Learning Technique","This research paper explores the application of machine learning (ML) techniques for cheiloscopic pattern detection and classification, with a focus on correlating these patterns with age and gender in forensic odontology. The study details a methodology involving lip impression transfer, image segmentation using MATLAB software, and feature extraction (area, diameter, extent, perimeter). Neural networks were employed for training, and test prints were classified into types I-V using Suzuki and Tsuchihashi's method. Results indicate significant differences in assessed parameters and lip print patterns between genders, highlighting the potential of ML-based cheiloscopy to enhance uniqueness in forensic identification. The study concludes that this novel ML-based technique of cheiloscopic recognition, for the first time, offers promising outcomes and new panoramas for personal identification and gender determination in the era of global digitization.","Cheiloscopic Characteristics detection and Pattern Classification by Machine Learning Technique  \nAuthors: Dominic Augustine, Sowmya SV  \nBackground: Machine Learning (ML) is afield enhancing the rapid growth of technology. Though use of digital softwares for cheiloscopic investigations have been attempted with limited success, the use of ML based techniques are scarce and seldom have been employed in forensic odontology. The present study aimed to identify cheiloscopic patterns through machine learning based methods and to correlate the segmented patterns with age and gender of individuals.  \nMethodology: A lip impression was made after applying dark lipstick and transferred to a white paper to record the wrinkles and grooves without smudging. The images obtained were photographed and subjected to MATLAB software analysis. The lip outlines were extracted using image segmentation. Parameters like area, diameter, extent and perimeter were assessed. Neural networks were used to train the patterns, later the test prints were subjected for classification into type I-V (Suzuki and Tsuchihashi's method) .  \nInput, segmentation and feature extraction  \nPoster No 23 – South Jordan Campus  \nTraining Plot Depicts Validation Accuracy of 91.67%  \nResults: Significant differences were observed in the parameters assessed and lip print patterns in both genders. ML based techniques enhance the uniqueness of cheiloscopy in forensic identification.  \nConclusion: For the first time a novel ML based technique of cheiloscopic recognition was performed with promising outcomes. In the ever-evolving age of global digitization, cheiloscopic evaluation through AI and ML could offer new panoramas for personal identification of individuals and gender determination.","cbCaiet73hlxfIGa","https://ap.wps.com/l/cbCaiet73hlxfIGa","pdf",264936,1,"English","en",105,"# Cheiloscopic Characteristics detection and Pattern Classification by Machine Learning Technique\n## Background\n## Methodology\n### Input, segmentation and feature extraction\n## Results\n## Conclusion","[{\"question\":\"What is the primary aim of the study?\",\"answer\":\"The study aimed to identify cheiloscopic patterns using machine learning techniques and correlate these patterns with an individual's age and gender in the field of forensic odontology.\"},{\"question\":\"How were lip impressions processed in the study?\",\"answer\":\"Lip impressions were made using dark lipstick, transferred to paper, photographed, and then analyzed using MATLAB software for image segmentation and feature extraction, followed by neural network training.\"},{\"question\":\"What were the key findings regarding gender differences?\",\"answer\":\"The study observed significant differences in assessed parameters and lip print patterns between genders, suggesting that ML-based cheiloscopy can enhance uniqueness for forensic identification.\"}]","Cheiloscopic Characteristics detection and Pattern Classification by Machine Learning Technique | 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is the primary aim of the study?","Question",{"text":73,"@type":74},"The study aimed to identify cheiloscopic patterns using machine learning techniques and correlate these patterns with an individual's age and gender in the field of forensic odontology.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How were lip impressions processed in the study?",{"text":78,"@type":74},"Lip impressions were made using dark lipstick, transferred to paper, photographed, and then analyzed using MATLAB software for image segmentation and feature extraction, followed by neural network training.",{"name":80,"@type":71,"acceptedAnswer":81},"What were the key findings regarding gender differences?",{"text":82,"@type":74},"The study observed significant differences in assessed parameters and lip print patterns between genders, suggesting that ML-based cheiloscopy can enhance uniqueness for forensic 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