Design and implementation of a weapon storage access control system based on hand gesture recognition and face recognition on Raspberry Pi 5 - Daffa Rahman; Sunarta Sunarta; Bagus Hendra Saputra (Institute of Computer Science (IOCS), 2026)
This study presents a multimodal biometric access control system for weapon storage facilities, integrating hand gesture recognition and face recognition through a sequential fusion architecture on Raspberry Pi 5. The sequential design activates face verification only after correct gesture authentication, optimizing computational efficiency on edge hardware while establishing a dual-layer security barrier. The gesture module combines MediaPipe Hands landmark extraction with LSTM-based temporal classification, achieving near-perfect accuracy across four gesture classes. The face module employs dlib's ResNet-34 for 128-dimensional embedding comparison, with an empirically recalibrated Euclidean distance threshold of 0.34 to eliminate false acceptance risks identified during intrusion testing. Evaluation under controlled conditions yielded 0% False Reject Rate and 0% False Accept Rate across 60 trials, with reliable GPIO-controlled solenoid actuation. Results demonstrate that sequential fusion of behavioral and physiological biometrics on a single edge device provides a viable security solution for high-risk access control applications.