Syn-DermaID: A Dual-Stream Framework for Robust Mobile Biometrics and Skin Lesion Profiling via 3D Synthetic Augmentation

Syn-DermaID: A Dual-Stream Framework for Robust Mobile Biometrics and Skin Lesion Profiling via 3D Synthetic Augmentation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 March 2026 V1 Latest version Share on Syn-DermaID: A Dual-Stream Framework for Robust Mobile Biometrics and Skin Lesion Profiling via 3D Synthetic Augmentation Authors : Zhang Wei 0009-0004-2563-3290 [email protected] , Lin Jia Wen , and Chen Zhi Hao Authors Info & Affiliations https://doi.org/10.22541/au.177281988.88757994/v1 90 views 39 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The deployment of deep learning models on mobile devices for critical applications-specifically high-fidelity biometric authentication and dermatological screening-is frequently hindered by two opposing forces: the scarcity of diverse, privacy-compliant training data and the computational limitations of edge hardware. This paper proposes Syn-DermaID, a unified framework that leverages generative 3D modeling to solve data scarcity in both domains. By utilizing a multi-array depth-sensing approach standard in modern smartphones, we reconstruct 3D surface maps of the target region. These maps inform a novel Topological Generative Adversarial Network (TopoGAN) to synthesize photorealistic training samples. Our mathematical analysis and experimental results demonstrate that models trained on this "hybrid-synthetic" data achieve state-of-the-art accuracy in unconstrained palmprint recognition (TAR > 99.2% @ FAR=0.01%) and fine-grained skin lesion classification (AUC > 0.94), even under challenging illumination conditions. Supplementary Material File (syn-dermaid.pdf) Download 326.21 KB Information & Authors Information Version history V1 Version 1 06 March 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords 3d synthetic augmentation dual-stream framework mobile biometrics skin lesion profiling topological gan Authors Affiliations Zhang Wei 0009-0004-2563-3290 [email protected] Neusoft University of Information View all articles by this author Lin Jia Wen Neusoft University of Information View all articles by this author Chen Zhi Hao Neusoft University of Information View all articles by this author Metrics & Citations Metrics Article Usage 90 views 39 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zhang Wei, Lin Jia Wen, Chen Zhi Hao. Syn-DermaID: A Dual-Stream Framework for Robust Mobile Biometrics and Skin Lesion Profiling via 3D Synthetic Augmentation. Authorea . 06 March 2026. DOI: https://doi.org/10.22541/au.177281988.88757994/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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