The imperative pursuit of computer vision technology research lies in developing a more accurate, intelligent, swift, and secure gesture imaging recognition methodology. In this context, we have devised an advanced intelligent elevator control interaction system that seamlessly integrates gesture and palmprint recognition. The system employs the Multi-Feature Robust Alignment Technique (MFRAT) to extract distinctive palmprint features, utilizing point-pair regions for palmprint matching. Gesture recognition is achieved through a sophisticated hybrid modeling approach, incorporating mediapipe and Resnet34. Subsequently, a dynamic frequency localization method is employed to translate and analyze specific elevator gesture commands, effectively executing user instructions and ensuring precise floor navigation. Experimental results underscore the system’s proficiency, with a recognition rate of up to 95.5% for single-frame gesture images and 92.84% for combined gesture images. By harnessing the diversity of gestures and the unique characteristics of palmprints, the system employs non-contact gesture imaging to facilitate seamless, efficient, and secure human-machine interaction between users and elevators, showcasing promising applications across various domains.