Synergizing Handcrafted and Deep Features for Enhanced Face Presentation Attack Detection

Synergizing Handcrafted and Deep Features for Enhanced Face Presentation Attack Detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Synergizing Handcrafted and Deep Features for Enhanced Face Presentation Attack Detection B. H. Shekar, Vannurswamy K, Bharathi Pilar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4738035/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper presents an integrated approach to enhance face presentation attack detection (PAD) by combining handcrafted Multi-level Local Binary Patterns (MLBP) and VGG16 deep learning features. Principal Component Analysis (PCA) is employed to reduce the dimensionality of the feature space, ensuring an efficient detection process. MLBP captures intricate details and patterns crucial for distinguishing between genuine and forged facial presentations. By analyzing texture patterns at multiple scales and focusing on both local texture variations and global features, MLBP effectively identifies specific characteristics that are not easily discernible with global features alone. VGG16, a convolutional neural network with 16 layers, is renowned for its deep architecture, which extracts high-level hierarchical features from facial images. It captures complex and hierarchical representations of image data, effectively differentiating between subtle variations in facial images. The features are integrated and then processed using PCA to reduce dimensionality, enhancing overall efficiency. Extensive experimentation is done on various datasets, including CS-MAD Mobile, CASIA FASD, and NUAA, demonstrating the success of the proposed approach. We have also conducted a comparative analysis with state-of-the-art literature. The comparison results demonstrate that this integrated approach distinguishes between genuine and fraudulent facial presentations more effectively than well-known methods in the field. Face Presentation attack detection (PAD) Attack Detection SVM PCA Pre-Trained Model VGG16 Multi-level Local Binary Pattern. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4738035","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330686164,"identity":"b37cc9c4-df8b-49b2-b3d2-e48d0ecf09d2","order_by":0,"name":"B. H. 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Principal Component Analysis (PCA) is employed to reduce the dimensionality of the feature space, ensuring an efficient detection process. MLBP captures intricate details and patterns crucial for distinguishing between genuine and forged facial presentations. By analyzing texture patterns at multiple scales and focusing on both local texture variations and global features, MLBP effectively identifies specific characteristics that are not easily discernible with global features alone. VGG16, a convolutional neural network with 16 layers, is renowned for its deep architecture, which extracts high-level hierarchical features from facial images. It captures complex and hierarchical representations of image data, effectively differentiating between subtle variations in facial images. The features are integrated and then processed using PCA to reduce dimensionality, enhancing overall efficiency. 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