{"version":"1.0","provider_name":"BUM","provider_url":"https:\/\/bum-apeiron.com","author_name":"admin","author_url":"https:\/\/bum-apeiron.com\/index.php\/author\/jita-au-com\/","title":"Using 3D Models for Improving Face Recognition - BUM","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"fCMK8kQLbd\"><a href=\"https:\/\/bum-apeiron.com\/index.php\/2024\/04\/10\/using-3d-models-for-improving-face-recognition\/\">Using 3D Models for Improving Face Recognition<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/bum-apeiron.com\/index.php\/2024\/04\/10\/using-3d-models-for-improving-face-recognition\/embed\/#?secret=fCMK8kQLbd\" width=\"600\" height=\"338\" title=\"&#8220;Using 3D Models for Improving Face Recognition&#8221; &#8212; BUM\" data-secret=\"fCMK8kQLbd\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/bum-apeiron.com\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","thumbnail_url":"https:\/\/bum-apeiron.com\/wp-content\/uploads\/2024\/03\/cover_issue_949_en_US.jpg","thumbnail_width":595,"thumbnail_height":793,"description":"Vol. 4 No. 2 (2014): JITA &#8211; APEIRON Zoran Bikicki, Ivan Milenkovi\u0107, Du\u0161an Star\u010devi\u0107 Using 3D Models for Improving Face Recognition Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT1402055B Download Article PDF Abstract Face recognition algorithm Principal Component Analysis (PCA) has a significant performance drop when comparing photographs taken from different angle. In this paper a 3D model was used for improving that performance. Model enables us to transform the face image which is taken from certain angle to en face. Model has been tested against biometric database formed at the Faculty of Organizational Sciences. Image rotation based on the model was performed before matching with the en face images from the database. Study results show that algorithm precision on biometric verification and identification has been seriously improved. Keywords: biometrics, face recognition, 3D graphics, PCA. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators."}