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This paper introduces IGate, a security-focused control stack for drones that restricts gesture commands to verified operators using few-shot face embedding verification. The study demonstrates that while offline accuracy is high, in-flight conditions significantly degrade face verification performance, highlighting the challenges of deploying computer vision in dynamic environments. By combining identity gating with an RBF-SVM classifier for hand landmarks, the system addresses critical safety concerns in shared indoor spaces without requiring extensive prior user training.
A Deployment Study of Identity-Gated Drone Gesture Control
arXiv:2609.25511v1 Announce Type: new 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.
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.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2609.25511 [cs.RO]
(or arXiv:2609.25511v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.25511 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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