From Heavy to Lean: A Pruning-Based Approach for Resource-Efficient rPPG Deep Networks

From Heavy to Lean: A Pruning-Based Approach for Resource-Efficient rPPG Deep Networks | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 4 December 2025 V1 Latest version Share on From Heavy to Lean: A Pruning-Based Approach for Resource-Efficient rPPG Deep Networks Authors : Dahui Dong , Aditya Shah , Nikhil Jain , and Ping Li 0009-0004-0063-4473 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176488096.68776030/v1 277 views 227 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Remote Photoplethysmography (rPPG) enables fully contactless estimation of physiological signals-such as heart rate-by analyzing subtle skin-color fluctuations captured through standard video cameras. Its non-invasive, unobtrusive nature makes rPPG particularly attractive for continuous monitoring in healthcare, fitness, and human-computer interaction scenarios. However, despite recent progress, state-of-the-art rPPG models remain computationally expensive and highly data dependent, posing significant challenges for real-time deployment on low-power or embedded platforms. To address these limitations, this work presents a pruning-based optimization framework specifically tailored for rPPG neural networks. The approach first establishes a strong baseline through comprehensive pre-training, then systematically eliminates redundant or low-impact parameters using structured criteria such as weight magnitude and parameter importance. A targeted fine-tuning stage subsequently restores model accuracy while reinforcing robustness. This framework yields compact, efficient rPPG models capable of maintaining high performance under tight computational and data constraints. Supplementary Material File (from_heavy_to_lean__a_pruning_based_approach_for_resource_efficient_rppg_deep_networks.pdf) Download 483.95 KB Information & Authors Information Version history V1 Version 1 04 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning machine learning model compression techniques remote photoplethysmography (rppg) video processing Authors Affiliations Dahui Dong Heilongjiang University View all articles by this author Aditya Shah Czech Technical University View all articles by this author Nikhil Jain Czech Technical University View all articles by this author Ping Li 0009-0004-0063-4473 [email protected] Czech Technical University View all articles by this author Metrics & Citations Metrics Article Usage 277 views 227 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dahui Dong, Aditya Shah, Nikhil Jain, et al. From Heavy to Lean: A Pruning-Based Approach for Resource-Efficient rPPG Deep Networks. Authorea . 04 December 2025. DOI: https://doi.org/10.22541/au.176488096.68776030/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 . 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