A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network

A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network | 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 A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network BUDDANNAGARI LATHA, A. K. VELMURUGAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4006196/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 Face sketch synthesis phenomenon, a kind of image-image translation, generates synthesized face/sketch with wide range of applications pertaining law enforcement and entertainment to mention few. Despite the compelling results produced by many existing methods of late, there are still challenges due to deformation and blurred effects on facial components resulting in unrealistic face/sketch. To overcome this problem, in this paper, we proposed a novel framework known as Deep Face-Sketch Synthesis Framework (DFSSF). The framework is realized with different building blocks including an algorithm known as Deep Face-Sketch Synthesis for High Perceptual Quality (DFSS-HPQ). The framework is based on the architecture of Generative Adversarial Network (GAN) which exploits facial structures and a novel labelling mechanism. It takes paired inputs compromising of face images and sketches. The framework also considers extraction of GANs with heterogeneity from inputs. Afterwards, they are stacked to obtain additional features that can be effectively used to rectify defects if any. Two algorithms known as Hybrid GAN for Face Sketch Synthesis (HGAN-FSS) and Stacked Hybrid GAN for Face Sketch Synthesis (SHGAN-FSS) are proposed. We used two frequently used datasets namely CUFS and CUFSF having samples collected from 606 and 1194 persons respectively. The proposed framework is built using Python data science platform. Empirical results of the framework are evaluated and compared with traditional face sketch methods, deep learning models and deep learning models based on GANs. The proposed framework showed better performance over the state of the art in presence of different styles, lighting conditions and head poses. Face-sketch synthesis deep learning image-image translation stacking generative adversarial network 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-4006196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275970198,"identity":"d9053e4a-924b-469f-b015-ed84976504db","order_by":0,"name":"BUDDANNAGARI LATHA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACPgYGNgjrQAL7hw8GNnJg9gM8WtgYmOFa2BhnVKQZQ9jEamHmOXM4sQHEwauF/fyxBz8Y7snxHc8xezizLS19ftjhh0Bb7OR0G3Bo4UlmN+xhKDaWPPPG3OBjm03uxttpBkAtycZmB3A5LJlNgochIXHDjRwDSaAtuRtnJ4C0HEjchksL/2M2yT8MCfUgLdK8bYfTDWenf8CvRSKZTRpoS4LBjRwzaaD3E+SlcwjYIvHYTFrGIMFw5plnxYbAQDbcIJ1TcCDBALdf+PkTn0m+qUiQ5zuevPEBMCrl5Wenb/7wocJODpcWCDAAERwGEPYBuAhBwP4ATMk3EKV6FIyCUTAKRhAAAIFGYuyrAGFBAAAAAElFTkSuQmCC","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":true,"prefix":"","firstName":"BUDDANNAGARI","middleName":"","lastName":"LATHA","suffix":""},{"id":275970199,"identity":"be4fcf59-c0d3-4e0a-adc5-6fa06436fab4","order_by":1,"name":"A. 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Despite the compelling results produced by many existing methods of late, there are still challenges due to deformation and blurred effects on facial components resulting in unrealistic face/sketch. To overcome this problem, in this paper, we proposed a novel framework known as Deep Face-Sketch Synthesis Framework (DFSSF). The framework is realized with different building blocks including an algorithm known as Deep Face-Sketch Synthesis for High Perceptual Quality (DFSS-HPQ). The framework is based on the architecture of Generative Adversarial Network (GAN) which exploits facial structures and a novel labelling mechanism. It takes paired inputs compromising of face images and sketches. The framework also considers extraction of GANs with heterogeneity from inputs. Afterwards, they are stacked to obtain additional features that can be effectively used to rectify defects if any. Two algorithms known as Hybrid GAN for Face Sketch Synthesis (HGAN-FSS) and Stacked Hybrid GAN for Face Sketch Synthesis (SHGAN-FSS) are proposed. We used two frequently used datasets namely CUFS and CUFSF having samples collected from 606 and 1194 persons respectively. The proposed framework is built using Python data science platform. Empirical results of the framework are evaluated and compared with traditional face sketch methods, deep learning models and deep learning models based on GANs. 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