arXiv:2611.05719v1 Announce Type: new 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.