Task-Aware Adaptive Neural Preprocessing for Robust Biometric and Object Recognition in Constrained Imaging Systems

Task-Aware Adaptive Neural Preprocessing for Robust Biometric and Object Recognition in Constrained Imaging Systems | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 23 March 2026 V1 Latest version Share on Task-Aware Adaptive Neural Preprocessing for Robust Biometric and Object Recognition in Constrained Imaging Systems Authors : Li Wei 0009-0007-4488-6105 [email protected] , Zhang Yixing , and Wang Fang Authors Info & Affiliations https://doi.org/10.22541/au.177429400.07386973/v1 98 views 73 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Current computer vision systems deployed on consumer hardware often suffer significant performance degradation when subjected to non-ideal environmental conditions, such as extreme lighting, low-resolution sensors, or high-dynamic-range (HDR) thermal inputs. Conventional pipelines treat Image Signal Processing (ISP) and downstream feature extraction as distinct, disjoint stages, leading to suboptimal information retention during bit-depth reduction (e.g., 16-bit to 8-bit conversion). This paper proposes the Adaptive Signal-to-Feature Network (AS2F-Net), a unified, end-to-end Hybrid Neural Architecture that jointly learns optimal signal tone-mapping and semantic feature extraction. By introducing a differentiable Neural ISP Block, the system adapts raw sensor inputs to maximize the accuracy of downstream tasks-specifically biometric authentication and hazard detection—on resource-constrained edge devices. We validate our approach through extensive experimentation, demonstrating that AS2F-Net outperforms static ISP baselines by substantial margins in low-light and high-contrast scenarios while reducing inference latency by approximately 15% compared to standard ResNet backbones. Furthermore, we provide a rigorous mathematical analysis of the quantization noise suppression and establish a theoretical bound on the gradient variance during Mixed-Precision Training. Supplementary Material File (biometric_neural.pdf) Download 690.35 KB Information & Authors Information Version history V1 Version 1 23 March 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords biometrics deep learning edge computing high dynamic range neural isp quantization aware training thermal imaging Authors Affiliations Li Wei 0009-0007-4488-6105 [email protected] Wuchang University of Technology View all articles by this author Zhang Yixing Wuchang University of Technology View all articles by this author Wang Fang Chongqing Institute of Engineering View all articles by this author Metrics & Citations Metrics Article Usage 98 views 73 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Li Wei, Zhang Yixing, Wang Fang. Task-Aware Adaptive Neural Preprocessing for Robust Biometric and Object Recognition in Constrained Imaging Systems. Authorea . 23 March 2026. 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