Identity gated gesture control improves drone safety indoors
A Deployment Study of Identity-Gated Drone Gesture Control
RoboticsComputer Vision and Pattern Recognition
Summary
Using hand gestures to control drones can be risky when multiple people are around because the drone might listen to the wrong person. The authors designed a system called IGate that makes the drone only respond to gestures from a recognized face. This system learns a user's face quickly without special training and tracks it to make sure gestures come from the right person. They tested the method on a small drone and found it made face recognition and gesture control more accurate and safer in real indoor flights.
What this means in practice
- •For drone manufacturers: Build drones that only respond to authorized users' hand gestures for safer indoor operation.$Commercial implications: Enables consumer and enterprise drone makers to develop secure gesture-based controls preventing unauthorized commands indoors.
- •For smart home system integrators: Integrate face-verified gesture inputs to control indoor drones or devices safely among multiple residents.
Authors
Diyari Mohammed Salih, Ilyes Chaabeni, Naima Ait Oufroukh
Abstract
Vision-based gesture control accepts commands from any hand in the camera field of view, which is unsafe in shared indoor spaces. This paper presents IGate, an identity-gated control stack that includes gesture control and face tracking, in which commands are admitted only when an enrolled operator is verified. The system performs few-shot user enrolment from 20 initial face frames, without prior user-specific training: verification compares an embedding of the current face crop against the enrolled template by cosine similarity, while face tracking uses proportional correction. Gesture control is achieved by classifying extracted hand landmarks using an RBF-SVM trained on a custom dataset. Additionally, a hierarchical finite-state machine handles mode selection, default, and fallback behaviours. The approach is tested on a DJI Tello EDU, each component evaluated offline and in-flight across 270 trials (149 flown). Face verification yields a 0.32% offline equal error rate versus 19.3% in-flight. Under hover-locked conditions, the RBF-SVM gesture classifier outperforms the geometric rule (0.850 vs. 0.651 accuracy), with 82% of this gap stemming from the depth channel. All logs and reproduction scripts will be released.