{"id":54393,"date":"2022-08-31T14:03:19","date_gmt":"2022-08-31T12:03:19","guid":{"rendered":"https:\/\/www.iemn.fr\/?p=54393"},"modified":"2022-09-01T11:22:09","modified_gmt":"2022-09-01T09:22:09","slug":"cnn-based-facial-aesthetics-analysis-through-dynamic-robust-losses-and-ensemble-regression","status":"publish","type":"post","link":"https:\/\/www.iemn.fr\/en\/newsletter\/cnn-based-facial-aesthetics-analysis-through-dynamic-robust-losses-and-ensemble-regression.html","title":{"rendered":"CNN Based Facial Aesthetics Analysis through Dynamic Robust Losses and Ensemble Regression"},"content":{"rendered":"<section  class='av_textblock_section av-l7hkjq14-c08352cfd4dcc8d7f97a640a2ded15f9'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p style=\"text-align: center;\"><a href=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-54394 size-full\" src=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale.jpg\" alt=\"\" width=\"600\" height=\"145\" srcset=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale.jpg 600w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale-300x73.jpg 300w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale-18x4.jpg 18w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/a><\/p>\n<\/div><\/section>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-51b454c2581d365e0ef24dacb3f1c7c4\">\n#top .av-special-heading.av-av_heading-51b454c2581d365e0ef24dacb3f1c7c4{\npadding-bottom:10px;\ncolor:#4392e8;\n}\nbody .av-special-heading.av-av_heading-51b454c2581d365e0ef24dacb3f1c7c4 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-51b454c2581d365e0ef24dacb3f1c7c4 .special-heading-inner-border{\nborder-color:#4392e8;\n}\n.av-special-heading.av-av_heading-51b454c2581d365e0ef24dacb3f1c7c4 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-51b454c2581d365e0ef24dacb3f1c7c4 av-special-heading-h3 custom-color-heading blockquote modern-quote modern-centered  avia-builder-el-1  el_after_av_textblock  el_before_av_one_half'><h3 class='av-special-heading-tag'  itemprop=\"headline\"  >Convolutional Neural Network (CNN) Based Facial Aesthetics Analysis through Dynamic Robust Losses and Ensemble Regression<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-10qf1p5-7b69d8b497882f34f319a0712b118c1d\">\n.flex_column.av-10qf1p5-7b69d8b497882f34f319a0712b118c1d{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 20px 0px;\n}\n<\/style>\n<div  class='flex_column av-10qf1p5-7b69d8b497882f34f319a0712b118c1d av_one_half  avia-builder-el-2  el_after_av_heading  el_before_av_one_half  first flex_column_div'     ><section  class='av_textblock_section av-l7hklx67-0eb9b2c59ff6cf2fb4de0bbaa4f13db3'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><h5>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-qawvnx-7d04b1873367d0f68a2c0268a256da48\">\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48{\ncolor:#7bb0e7;\nborder-color:#7bb0e7;\n}\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48 .av-icon-char{\nfont-size:19px;\nline-height:19px;\n}\n<\/style>\n<span  class='av_font_icon av-qawvnx-7d04b1873367d0f68a2c0268a256da48 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue885' data-av_iconfont='entypo-fontello' ><\/span><\/span><\/h5>\n<p style=\"font-weight: 400;\">The search for beauty has been pursued by mankind since its beginnings. The attempt to trace the secret of beauty has been a goal for philosophers, artists and scientists throughout human history. Nowadays, the beauty of the face receives even more interest due to the rapid development of plastic surgery and the cosmetics industry.<strong> In the last decade, several studies have shown that facial attractiveness can be learned by machines.<\/strong> Indeed, facial beauty prediction is sophisticated task even for human, where for one face distinct facial beauty scores can be given by different people. Thus, facial beauty prediction (FBP) has high subjective bias. On the other hand, big labelled data is required to create an efficient machine learning system for Facial beauty prediction, especially for deep learning methods.<\/p>\n<h5>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-qawvnx-7d04b1873367d0f68a2c0268a256da48\">\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48{\ncolor:#7bb0e7;\nborder-color:#7bb0e7;\n}\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48 .av-icon-char{\nfont-size:19px;\nline-height:19px;\n}\n<\/style>\n<span  class='av_font_icon av-qawvnx-7d04b1873367d0f68a2c0268a256da48 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue885' data-av_iconfont='entypo-fontello' ><\/span><\/span><\/h5>\n<p style=\"font-weight: 400;\">The goal of this work is to leverage the advances in Deep Learning architectures to provide stable and accurate face beauty estimation\u00a0from static face images.<\/p>\n<h5>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-qawvnx-7d04b1873367d0f68a2c0268a256da48\">\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48{\ncolor:#7bb0e7;\nborder-color:#7bb0e7;\n}\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48 .av-icon-char{\nfont-size:19px;\nline-height:19px;\n}\n<\/style>\n<span  class='av_font_icon av-qawvnx-7d04b1873367d0f68a2c0268a256da48 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue885' data-av_iconfont='entypo-fontello' ><\/span><\/span><\/h5>\n<p style=\"font-weight: 400;\"><strong>In this work, we propose a system that exploits the diversity of learners as shown in Figure 1. We present two main proposals. First, we propose to combine two different CNN architectures into a single architecture (called the two-branch architecture) that is trained end-to-end. Second, we propose to build an ensemble of regressions where the final prediction is given by the average of all predictions. The latter solution does not need to be trained on new validation sets. More specifically, we propose ensemble regressions using one-branch architectures (ResneXt-50 and Inception-v3) and our proposed two-branch architecture (REX-INCEP) trained with different loss functions.<\/strong> Four loss functions are used in our approach, namely MSE, dynamic ParamSmoothL1, dynamic Huber and dynamic Tukey.<\/p>\n<\/div><\/section><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-qmiwrd-62499ca73b245e58b24e71429a88049a\">\n.flex_column.av-qmiwrd-62499ca73b245e58b24e71429a88049a{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-qmiwrd-62499ca73b245e58b24e71429a88049a av_one_half  avia-builder-el-7  el_after_av_one_half  el_before_av_textblock  flex_column_div av-zero-column-padding'     ><section  class='av_textblock_section av-l7hklx67-0eb9b2c59ff6cf2fb4de0bbaa4f13db3'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><h5>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-qawvnx-7d04b1873367d0f68a2c0268a256da48\">\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48{\ncolor:#7bb0e7;\nborder-color:#7bb0e7;\n}\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48 .av-icon-char{\nfont-size:19px;\nline-height:19px;\n}\n<\/style>\n<span  class='av_font_icon av-qawvnx-7d04b1873367d0f68a2c0268a256da48 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue885' data-av_iconfont='entypo-fontello' ><\/span><\/span><\/h5>\n<p style=\"font-weight: 400;\">\u00a0Our approach is evaluated <a href=\"https:\/\/arxiv.org\/abs\/1801.06345\" target=\"_blank\" rel=\"noopener\">on the SCUT-FBP5500 database<\/a> using the two evaluation scenarios provided by the database creators: 60%-40% split and five-fold cross-validation. In both evaluation scenarios, our approach outperforms the state of the art on several metrics. These comparisons highlight the effectiveness of the proposed solutions for FBP. They also show that the proposed dynamic robust losses lead to more flexible and accurate estimators (<a href=\"https:\/\/github.com\/faresbougourzi\/CNN-ER_for_FBP\" target=\"_blank\" rel=\"noopener\">https:\/\/github.com\/faresbougourzi\/CNN-ER_for_FBP<\/a>).<\/p>\n<h5>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-qawvnx-7d04b1873367d0f68a2c0268a256da48\">\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48{\ncolor:#7bb0e7;\nborder-color:#7bb0e7;\n}\n.av_font_icon.av-qawvnx-7d04b1873367d0f68a2c0268a256da48 .av-icon-char{\nfont-size:19px;\nline-height:19px;\n}\n<\/style>\n<span  class='av_font_icon av-qawvnx-7d04b1873367d0f68a2c0268a256da48 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue885' data-av_iconfont='entypo-fontello' ><\/span><\/span><\/h5>\n<p><span style=\"color: #4392e8;\">This collaborative work are between several research teams from :<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\">Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, Lecce, 73100, Italy. ( Dr Fares Bougourzi, PostDoc)<\/li>\n<li style=\"font-weight: 400;\">University of the Basque Country UPV\/EHU, San Sebastian 20018, Basque Country, Spain, IKERBASQUE, Basque Foundation for Science, Bilbao, 48012, Basque Country, Spain (Pr Fadi Dornaika).<\/li>\n<li style=\"font-weight: 400;\">Universite Polytechnique Hauts-de-France, Universit\u00e9 de Lille, CNRS, UMR 8520, Valenciennes, 59313, Hauts-de-France, France ( Pr Abdelmalik Taleb-Ahmed).<\/li>\n<\/ul>\n<p>The approach developed and the first results obtained have been published in reference [1].<\/p>\n<p><em>\u00a0[1] F. Bougourzi, F. Dornaika, A. Taleb-Ahmed,\u00a0\u00a0 Deep learning based face beauty prediction via dynamic robust losses and ensemble regression, Knowledge-Based Systems, Vol \u00a0242, pp 108246, 2022.<\/em><\/p>\n<\/div><\/section><\/div><section  class='av_textblock_section av-l7hl59i2-d18b1e5bf72b6ce34b57363f1ea293c9'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p style=\"font-weight: 400; text-align: center;\"><strong><span style=\"color: #4392e8;\">Figure 1: Our Proposed EN-CNN approach.<\/span><\/strong><\/p>\n<p><a href=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale2.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-54401 size-full\" src=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale2.jpg\" alt=\"\" width=\"746\" height=\"1024\" srcset=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale2.jpg 746w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale2-219x300.jpg 219w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale2-9x12.jpg 9w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2022\/08\/beaute_faciale2-514x705.jpg 514w\" sizes=\"auto, (max-width: 746px) 100vw, 746px\" \/><\/a><\/p>\n<\/div><\/section><\/p>\n<section  class='av_textblock_section av-l7hks5zq-06080fdc6519ab5f0b783c3c3cb38845'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p style=\"text-align: center;\"><div  class='avia-button-wrap av-rpqvoq-721f891c0eafd9f18b1e661271382e60-wrap avia-button-left  avia-builder-el-13  el_before_av_button  avia-builder-el-first'><a href='mailto:abdelmalik.taleb-ahmed@uphf.fr'  class='avia-button av-rpqvoq-721f891c0eafd9f18b1e661271382e60 av-link-btn avia-icon_select-yes-left-icon avia-size-small avia-position-left avia-color-silver'   aria-label=\"abdelmalik.taleb-ahmed@uphf.fr\"><span class='avia_button_icon avia_button_icon_left' aria-hidden='true' data-av_icon='\ue805' data-av_iconfont='entypo-fontello'><\/span><span class='avia_iconbox_title' >abdelmalik.taleb-ahmed@uphf.fr<\/span><\/a><\/div> \u00a0 <div  class='avia-button-wrap av-rpqvoq-316ac125ef0360b26d43ad706a9a5989-wrap avia-button-left  avia-builder-el-14  el_after_av_button  el_before_av_button'><a href='mailto:fares.bougourzi@isasi.cnr.it'  class='avia-button av-rpqvoq-316ac125ef0360b26d43ad706a9a5989 av-link-btn avia-icon_select-yes-left-icon avia-size-small avia-position-left avia-color-silver'   aria-label=\"fares.bougourzi@isasi.cnr.it\"><span class='avia_button_icon avia_button_icon_left' aria-hidden='true' data-av_icon='\ue805' data-av_iconfont='entypo-fontello'><\/span><span class='avia_iconbox_title' >fares.bougourzi@isasi.cnr.it<\/span><\/a><\/div> \u00a0 <div  class='avia-button-wrap av-rpqvoq-41e6be86e86262070df21f6ce6f24080-wrap avia-button-left  avia-builder-el-15  el_after_av_button  avia-builder-el-last'><a href='mailto:fadi.dornaika@ehu.eus'  class='avia-button av-rpqvoq-41e6be86e86262070df21f6ce6f24080 av-link-btn avia-icon_select-yes-left-icon avia-size-small avia-position-left avia-color-silver'   aria-label=\"fadi.dornaika@ehu.eus\"><span class='avia_button_icon avia_button_icon_left' aria-hidden='true' data-av_icon='\ue805' data-av_iconfont='entypo-fontello'><\/span><span class='avia_iconbox_title' >fadi.dornaika@ehu.eus<\/span><\/a><\/div><\/p>\n<\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[297],"tags":[],"class_list":["post-54393","post","type-post","status-publish","format-standard","hentry","category-newsletter"],"_links":{"self":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/54393","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/comments?post=54393"}],"version-history":[{"count":0,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/54393\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/media?parent=54393"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/categories?post=54393"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/tags?post=54393"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}