{"id":1866,"date":"2026-10-06T15:47:36","date_gmt":"2026-10-06T15:47:36","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1866"},"modified":"2026-10-06T15:58:47","modified_gmt":"2026-10-06T15:58:47","slug":"vol24-no1-art5","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/","title":{"rendered":"Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks"},"content":{"rendered":"<p>Nat\u00e1lia Soares Gir\u00e3o <a href=\"https:\/\/orcid.org\/0000-0003-1749-9648\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, M\u00e1rcia Muller <a href=\"https:\/\/orcid.org\/0000-0002-4463-3526\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a> &amp; L\u00facia Val\u00e9ria Arruda <a href=\"https:\/\/orcid.org\/0000-0002-5704-8131\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Personal identification through biometric systems recognizes a person\u2019s unique biological or behavioral characteristics. For high-security systems, user identification requires reliable and robust biometric methods. This work develops a method for footstep biometric recognition, based on pressure-sensor signals and convolutional neural networks, for access to restricted areas. The biometric acquisition device is a platform instrumented with fiber Bragg gratings to capture optical signals associated with the plantar pressure distribution. A preprocessing algorithm transforms the gratings\u2019 wavelength shifts in the acquired signals into a plantar pressure map, separating the left and right feet into two smaller images. These small images are used to independently train two convolutional neural networks, one for each foot. After training, both CNNs are used as input to a fully connected neural network (FNN), forming a two-stage neural network (CNN-FNN) model whose weights are trained to identify a person correctly. This CNN-FNN biometric system identifies users from the complete plantar pressure map comprising both feet. The developed system achieves 98.67% accuracy, making it an efficient tool for inclusion in biometric security systems.<\/p>\n<p><strong>Keywords:<\/strong> Footstep Identification, Pressure Map, Fiber Bragg Gratings, Convolutional Neural Network.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol24-no1-art5\">10.21528\/lnlm-vol24-no1-art5<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2026\/12\/vol24-no1-art5.pdf\">vol24-no1-art5.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2026\/12\/vol24-no1-art5.bib\">vol24-no1-art5.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Nat\u00e1lia Soares Gir\u00e3o , M\u00e1rcia Muller &amp; L\u00facia Val\u00e9ria Arruda Abstract: Personal identification through biometric systems recognizes a person\u2019s unique biological or behavioral characteristics. For high-security systems, user identification requires reliable and robust biometric methods. This work develops a method <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1851,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1866","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks - 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\/vol24-no1\/vol24-no1-art5\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Nat\u00e1lia Soares Gir\u00e3o , M\u00e1rcia Muller &amp; L\u00facia Val\u00e9ria Arruda Abstract: Personal identification through biometric systems recognizes a person\u2019s unique biological or behavioral characteristics. For high-security systems, user identification requires reliable and robust biometric methods. This work develops a method Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta property=\"article:modified_time\" content=\"2026-10-06T15:58:47+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/\",\"name\":\"Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"datePublished\":\"2026-10-06T15:47:36+00:00\",\"dateModified\":\"2026-10-06T15:58:47+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#primaryimage\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 24 &#8211; N\u00famero 1\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"name\":\"Learning and NonLinear Models\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\",\"name\":\"Learning and NonLinear Models\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"width\":398,\"height\":94,\"caption\":\"Learning and NonLinear Models\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks - Learning and NonLinear Models","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/","og_locale":"pt_BR","og_type":"article","og_title":"Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks - Learning and NonLinear Models","og_description":"Nat\u00e1lia Soares Gir\u00e3o , M\u00e1rcia Muller &amp; L\u00facia Val\u00e9ria Arruda Abstract: Personal identification through biometric systems recognizes a person\u2019s unique biological or behavioral characteristics. For high-security systems, user identification requires reliable and robust biometric methods. This work develops a method Read More ...","og_url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/","og_site_name":"Learning and NonLinear Models","article_modified_time":"2026-10-06T15:58:47+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. tempo de leitura":"2 minutos"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/","url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/","name":"Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks - Learning and NonLinear Models","isPartOf":{"@id":"https:\/\/sbia.org.br\/lnlm\/#website"},"primaryImageOfPage":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#primaryimage"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#primaryimage"},"thumbnailUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg","datePublished":"2026-10-06T15:47:36+00:00","dateModified":"2026-10-06T15:58:47+00:00","breadcrumb":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/"]}]},{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#primaryimage","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg"},{"@type":"BreadcrumbList","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/vol24-no1-art5\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Browse issues","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/"},{"@type":"ListItem","position":2,"name":"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 24 &#8211; N\u00famero 1","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol24-no1\/"},{"@type":"ListItem","position":3,"name":"Footstep Identification on Pressure Sensitive Mat through Convolutional Neural Networks"}]},{"@type":"WebSite","@id":"https:\/\/sbia.org.br\/lnlm\/#website","url":"https:\/\/sbia.org.br\/lnlm\/","name":"Learning and NonLinear Models","description":"","publisher":{"@id":"https:\/\/sbia.org.br\/lnlm\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/sbia.org.br\/lnlm\/#organization","name":"Learning and NonLinear Models","url":"https:\/\/sbia.org.br\/lnlm\/","logo":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","width":398,"height":94,"caption":"Learning and NonLinear Models"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1866","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/comments?post=1866"}],"version-history":[{"count":1,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1866\/revisions"}],"predecessor-version":[{"id":1867,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1866\/revisions\/1867"}],"up":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1851"}],"wp:attachment":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/media?parent=1866"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}