{"id":1153,"date":"2019-11-20T15:37:31","date_gmt":"2019-11-20T17:37:31","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1153"},"modified":"2019-11-20T15:37:31","modified_gmt":"2019-11-20T17:37:31","slug":"vol17-no2-art1","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol17-no2\/vol17-no2-art1\/","title":{"rendered":"A  face recognition framework using self-adaptive differential evolution"},"content":{"rendered":"<p><strong><font size=\"+2\">Plichoski, G.F. <a href=\"http:\/\/orcid.org\/0000-0003-2297-7894\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/orcid-2.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Chidambaram, C.\u00a0<a href=\"http:\/\/orcid.org\/0000-0002-3093-0407\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/orcid-2.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Parpinelli, R.S.\u00a0<a href=\"http:\/\/orcid.org\/0000-0001-7326-5032\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/orcid-2.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/font><\/strong><\/p>\n<p align=\"justify\"><strong>Abstract:<\/strong> It is well known that the development of face recognition (FR) systems is challenging under uncontrolled conditions often related to the variation of pose, illumination, expression, and occlusion. Also, to collect the necessary amount of images is hard to guarantee in many situations, e.g. ID cards, drivers licenses or visas, leading to the one sample per person (OSPP) problem. This work addresses the OSPP problem along with illumination and pose variation using an FR framework composed of a self-adaptive Differential Evolution, named FRjDE. The main feature of the framework stands on the use of the optimization algorithm for choosing which preprocessing and feature extraction strategies to use, as well as tunning their parameters. Also, by using the jDE algorithm, F and CR control parameters are self-adapted. Experiments are made using two well-known databases, named CMU-PIE and FERET. Results obtained from the FRjDE approach are compared against the FR framework using the standard DE algorithm and against results found in the literature. Results suggest that the proposed approach is highly competitive and well suited for face recognition.<\/p>\n<p><strong>Keywords:<\/strong> Illumination variation, pose variation, continuous optimization, evolutionary algorithms, machine learning, parameter control<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol17-no2-art1\">10.21528\/lnlm-vol17-no2-art1<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/vol17-no2-art1.pdf\" rel=\"\">vol17-no2-art1.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/11\/vol17-no2-art1.bib\" rel=\"\">vol17-no2-art1.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Plichoski, G.F. , Chidambaram, C.\u00a0, Parpinelli, R.S.\u00a0 Abstract: It is well known that the development of face recognition (FR) systems is challenging under uncontrolled conditions often related to the variation of pose, illumination, expression, and occlusion. Also, to collect the <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol17-no2\/vol17-no2-art1\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1149,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1153","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>A face recognition framework using self-adaptive differential evolution - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol17-no2\/vol17-no2-art1\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A face recognition framework using self-adaptive differential evolution - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Plichoski, G.F. , Chidambaram, C.\u00a0, Parpinelli, R.S.\u00a0 Abstract: It is well known that the development of face recognition (FR) systems is challenging under uncontrolled conditions often related to the variation of pose, illumination, expression, and occlusion. 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