{"id":3515,"date":"2025-06-12T07:23:54","date_gmt":"2025-06-12T07:23:54","guid":{"rendered":"https:\/\/jita-au.com\/?p=3515"},"modified":"2025-09-24T11:40:57","modified_gmt":"2025-09-24T11:40:57","slug":"raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods","status":"publish","type":"post","link":"https:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/","title":{"rendered":"RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3515\" class=\"elementor elementor-3515\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e232701 elementor-section-height-min-height elementor-section-full_width elementor-hidden-tablet elementor-hidden-mobile elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"e232701\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cb97594\" data-id=\"cb97594\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a18c791 elementor-widget__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"a18c791\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 15 No. 1 (2025): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b2fff26 elementor-widget elementor-widget-heading\" data-id=\"b2fff26\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN<b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52044bb elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"52044bb\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c82606 elementor-widget elementor-widget-heading\" data-id=\"1c82606\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c297c9e elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"c297c9e\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-455c8d6 e-grid e-con-full wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"455c8d6\" data-element_type=\"container\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dcea8af elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"dcea8af\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0579899 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"0579899\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: https:\/\/doi.org\/10.7251\/JIT2501005P<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f3b63f elementor-align-center elementor-widget elementor-widget-button\" data-id=\"2f3b63f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/06\/Pages-from-JITA_Vol-15_Issue-1.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb7c4cb elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"cb7c4cb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-61aebe3 elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"61aebe3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c5f19e elementor-widget elementor-widget-heading\" data-id=\"5c5f19e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-85fffb3 elementor-widget elementor-widget-heading\" data-id=\"85fffb3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 29.4.2025.<br><b>Paper accepted:<\/b> 23.5.2025.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6dd494a wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"6dd494a\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb049d1 elementor-widget elementor-widget-shortcode\" data-id=\"cb049d1\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3515 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">1,116<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d4dbcb6 elementor-widget elementor-widget-shortcode\" data-id=\"d4dbcb6\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">Downloaded Article PDF: <span class=\"snr-download-count-num\" data-post-id=\"0\">0<\/span> times\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-511d0df elementor-section-height-min-height elementor-section-full_width elementor-hidden-mobile elementor-hidden-desktop elementor-hidden-laptop elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"511d0df\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0acee9c\" data-id=\"0acee9c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c0e98b3 elementor-widget__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"c0e98b3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 15 No. 1 (2025): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e6b43c5 elementor-widget elementor-widget-heading\" data-id=\"e6b43c5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN<b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68f1849 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"68f1849\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-186a6b2 elementor-widget elementor-widget-heading\" data-id=\"186a6b2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b2f501 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"7b2f501\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5027126 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"5027126\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a5312c elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"4a5312c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: https:\/\/doi.org\/10.7251\/JIT2501005P<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d7a912 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"8d7a912\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/06\/Pages-from-JITA_Vol-15_Issue-1.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-580ffd3 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"580ffd3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-211717e elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"211717e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62b72c3 elementor-widget elementor-widget-heading\" data-id=\"62b72c3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-40654e3 elementor-widget elementor-widget-heading\" data-id=\"40654e3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 29.4.2025.<br><b>Paper accepted:<\/b> 23.5.2025.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f4dcd16 wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"f4dcd16\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0f73093 elementor-widget elementor-widget-shortcode\" data-id=\"0f73093\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3515 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">1,116<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f97dc27 elementor-widget elementor-widget-shortcode\" data-id=\"f97dc27\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">Downloaded Article PDF: <span class=\"snr-download-count-num\" data-post-id=\"0\">0<\/span> times\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2d980b8 elementor-section-height-min-height elementor-section-full_width elementor-hidden-tablet elementor-hidden-desktop elementor-hidden-laptop elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"2d980b8\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d772afa\" data-id=\"d772afa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5597455 elementor-widget__width-inherit elementor-widget-mobile__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"5597455\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 15 No. 1 (2025): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f3e70ee elementor-widget elementor-widget-heading\" data-id=\"f3e70ee\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN<b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b0a72e2 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"b0a72e2\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87a3678 elementor-widget elementor-widget-heading\" data-id=\"87a3678\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96936f4 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"96936f4\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f04febc elementor-widget__width-initial elementor-widget-mobile__width-inherit elementor-widget elementor-widget-heading\" data-id=\"f04febc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-389228b elementor-widget__width-initial elementor-widget-mobile__width-inherit elementor-widget elementor-widget-heading\" data-id=\"389228b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: https:\/\/doi.org\/10.7251\/JIT2501005P<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73b8917 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"73b8917\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/06\/Pages-from-JITA_Vol-15_Issue-1.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80faf79 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"80faf79\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8cefffd elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"8cefffd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0c5e44b elementor-widget elementor-widget-heading\" data-id=\"0c5e44b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e6212b1 elementor-widget elementor-widget-heading\" data-id=\"e6212b1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 29.4.2025.<br><b>Paper accepted:<\/b> 23.5.2025.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dee52ab wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"dee52ab\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5cb9871 elementor-widget elementor-widget-shortcode\" data-id=\"5cb9871\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3515 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">1,116<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fe29ae9 elementor-widget elementor-widget-shortcode\" data-id=\"fe29ae9\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">Downloaded Article PDF: <span class=\"snr-download-count-num\" data-post-id=\"0\">0<\/span> times\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry Paper received: 29.4.2025.Paper accepted: 23.5.2025. Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry Paper received: 29.4.2025.Paper accepted: 23.5.2025. Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry Paper received: 29.4.2025.Paper accepted: 23.5.2025.<\/p>\n","protected":false},"author":1,"featured_media":3508,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3515","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS - JITA -Journal of Information Technology and Application<\/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:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS - JITA -Journal of Information Technology and Application\" \/>\n<meta property=\"og:description\" content=\"Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry Paper received: 29.4.2025.Paper accepted: 23.5.2025. Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry Paper received: 29.4.2025.Paper accepted: 23.5.2025. Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry Paper received: 29.4.2025.Paper accepted: 23.5.2025.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/\" \/>\n<meta property=\"og:site_name\" content=\"JITA -Journal of Information Technology and Application\" \/>\n<meta property=\"article:published_time\" content=\"2025-06-12T07:23:54+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-09-24T11:40:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/06\/JITA_Vol-15_Issue-1-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"2560\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\/\/jita-au.com\/#\/schema\/person\/d4becda53cfcbc99c449927eabf3877f\"},\"headline\":\"RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS\",\"datePublished\":\"2025-06-12T07:23:54+00:00\",\"dateModified\":\"2025-09-24T11:40:57+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/\"},\"wordCount\":703,\"publisher\":{\"@id\":\"https:\/\/jita-au.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/06\/JITA_Vol-15_Issue-1-scaled.jpg\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/\",\"url\":\"https:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/\",\"name\":\"RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS - 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JITA -Journal of Information Technology and Application","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:\/\/jita-au.com\/index.php\/2025\/06\/12\/raw-and-noisy-fingerprint-image-classification-with-natural-language-processing-techniques-and-ensemble-machine-learning-methods\/","og_locale":"en_US","og_type":"article","og_title":"RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS - JITA -Journal of Information Technology and Application","og_description":"Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry Paper received: 29.4.2025.Paper accepted: 23.5.2025. Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. Keywords: biometrics, ensemble learning, fingerprint, machine learning, natural language processing, stylometry Paper received: 29.4.2025.Paper accepted: 23.5.2025. Vol. 15 No. 1 (2025): JITA &#8211; APEIRON MILAN PANI\u0106, NEMANJA MA\u010cEK, BRANIMIR TRENKI\u0106, DANIEL MENI\u0106ANIN RAW AND NOISY FINGERPRINT IMAGE CLASSIFICATION WITH NATURAL LANGUAGE PROCESSING TECHNIQUES AND ENSEMBLE MACHINE LEARNING METHODS Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2501005P Download Article PDF Abstract This paper presents a raw and noisy fingerprint image recognition system based on natural language processing feature extraction methods and ensemble machine learning methods. The main goal of the proposed model is to reach state-of-the-art classification accuracy, even with the noisy images, eliminate false acceptance rates, and cancel the possibility of recreating a fake fingerprint image from a generated template. To achieve this, we omit preprocessing phase such as application of gradient vectors and multiple filter banks that are typically employed in traditional fingerprint recognition systems. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting raw fingerprint image into a one-dimensional set of fixed-length codes, which then undergoes stylometric extraction of features further being used for classification. The experimental evaluation shows that the system performs as intended. In addition, the computational and storage costs are significantly decreased with respect to traditional systems, which makes it suitable for use in practical applications. 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