[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123674-en":3,"doc-seo-123674-105":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},123674,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",6,"Technology","An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition - Implementation Summary","This study presents an optimized approach to improving facial recognition and masked facial recognition using machine learning and deep learning. Unlike prior work that relied on single models for classification without reporting optimal parameters, the framework uses grid search with hyperparameter tuning and nested cross-validation in the verification phase. Experiments are conducted on a large dataset containing both masked and unmasked facial images. Results show SVM with tuning achieves the highest accuracy of 0.99912, with strong precision for both settings. Real-life scenario tests confirm reliable masked identification. The framework enhances performance, generalization, and robustness and supports security use cases in public safety and healthcare.","An Optimized Machine Learning and Deep Learning Framework for Facial and  \nMasked Facial Recognition  \nABSTRACT  \nIn this study, we aimed to find an optimized approach to improving facial and masked facial recognition using machine learning and deep learning techniques. Prior studies only used a single machine learning model for classification and did not report optimal parameter values. In contrast, we utilized a grid search with hyperparameter tuning and nested cross-validation to achieve better results during the verification phase. We performed experiments on a large dataset of facial images with and without masks. Our findings showed that the SVM model with hyperparameter tuning had the highest accuracy compared to other models, achieving a recognition accuracy of 0.99912. The precision values for recognition without masks and with masks were 0.99925 and 0.98417, respectively. We tested our approach in real-life scenariosand found that it accurately identified masked individuals through facial recognition. Furthermore, our study stands out from others as it incorporates hyperparameter tuning and nested cross-validation during the verification phase to enhance the model's performance, generalization, and robustness while optimizing data utilization. Our optimized approach has potential implications for improving security systems in various domains, including public safety and healthcare.","cbCaicIFk4RmlLYL","https://ap.wps.com/l/cbCaicIFk4RmlLYL","pdf",40380,1,"English","en",105,"# Abstract\n## Optimization Strategy\n## Experimental Results\n## Real-World Verification","[{\"question\":\"What optimization methods does the framework use for masked face recognition?\",\"answer\":\"The framework applies grid search with hyperparameter tuning and nested cross-validation during the verification phase.\"},{\"question\":\"Which model achieved the best accuracy and what was the reported value?\",\"answer\":\"The SVM model with hyperparameter tuning achieved the highest accuracy, with a recognition accuracy of 0.99912.\"},{\"question\":\"How does performance differ between unmasked and masked face recognition?\",\"answer\":\"Precision for unmasked recognition is 0.99925, while precision for masked recognition is 0.98417, indicating strong but slightly lower performance for masked faces.\"}]","An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition - Implementation Summary | PDF",1785817942,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"an-optimized-machine-learning-and-deep-learning-framework-for-facial-and-masked-facial-recognition-implementation-summary","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/technology/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/an-optimized-machine-learning-and-deep-learning-framework-for-facial-and-masked-facial-recognition-implementation-summary/123674/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What optimization methods does the framework use for masked face recognition?","Question",{"text":73,"@type":74},"The framework applies grid search with hyperparameter tuning and nested cross-validation during the verification phase.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which model achieved the best accuracy and what was the reported value?",{"text":78,"@type":74},"The SVM model with hyperparameter tuning achieved the highest accuracy, with a recognition accuracy of 0.99912.",{"name":80,"@type":71,"acceptedAnswer":81},"How does performance differ between unmasked and masked face recognition?",{"text":82,"@type":74},"Precision for unmasked recognition is 0.99925, while precision for masked recognition is 0.98417, indicating strong but slightly lower performance for masked faces.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,111,116,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":109,"slug":110},50,"technology",{"id":112,"doc_module":4,"doc_module_name":45,"category_name":113,"show_sort_weight":114,"slug":115},7,"Healthcare",40,"healthcare",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":118,"show_sort_weight":119,"slug":120},8,"Research & Report",30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]