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arXiv · 2611.05719

Adaptive Virtual PIN-Button Recognition on Passive NFC Cards via Domain Adaptation and Uncertainty Quantification

Dickson Akuoko Sarpong·Hongzhi Guo
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Abstract

Passive Near Field Communication (NFC) cards are widely used for access control and authentication but provide no native interface for reliable user input. Radio-frequency (RF)-based interaction recognition offers a way to enable input on these cards, but hardware-dependent signal variations across cards and readers limit recognition reliability. This paper presents a practical virtual input framework that enables reliable PIN-button entry on passive NFC cards using a 12-button interface without modifying the commercial NFC card or the ISO/IEC 15693 communication protocol. We develop a multi-domain adaptation framework that jointly learns interaction representations invariant to both card and reader variations. To improve decision reliability, we introduce a model-agnostic uncertainty quantification framework based on conformal prediction that estimates recognition confidence. When confidence is insufficient, the system requests re-input; otherwise, accepted inputs are immediately confirmed through reader feedback, enabling reliable user interaction. A Software-Defined Radio (SDR)-based ISO/IEC 15693 testbed is developed using multiple commercial NFC cards and reader configurations. Experimental results show that domain adaptation improves recognition accuracy from 93.75% to 95.42% for the deep learning (DL) model and from 85.00% to 89.11% for the Random Forest model. Conformal prediction further enables adaptive high-confidence recognition, achieving average recognition accuracies of 96.85% and 90.55% for the DL and Random Forest models, respectively, across diverse hardware configurations.

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Dickson Akuoko Sarpong, Hongzhi Guo. 2026-11-03. Adaptive Virtual PIN-Button Recognition on Passive NFC Cards via Domain Adaptation and Uncertainty Quantification. https://arxiv.org/abs/2611.05719

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