Personalized Drone Interaction : Adaptive Hand Gesture Control with Facial Authentication

Abstract

This paper presents a novel system for personalized drone interaction, integrating adaptive hand gesture control with facial authentication. Utilizing the DJI Tello drone equipped with a 5 MP camera, the system employs advanced computer vision and machine learning techniques to ensure secure and intuitive control. Facial recognition using the Histogram of Oriented Gradients (HOG) method and FaceNet model verifies user identity, while MediaPipe and a custom convolutional neural network (CNN) facilitate accurate hand gesture recognition. The system’s real-time processing capabilities ensure seamless and responsive user interaction. Experimental results demonstrate the system’s robustness and accuracy in various scenarios, highlighting its potential for diverse applications such as security, entertainment, and personal assistance.