AI Iris Recognition Moves to the Edge
Homsh Technology has achieved a significant milestone by successfully deploying its lightweight iris recognition model on the domestic Rockchip RK3588 NPU platform. This breakthrough enables real-time iris recognition directly on edge devices without relying on cloud computing resources.
Technical Achievements
- Target Platform: Rockchip RK3588
- Model Type: Iris Recognition + Face Recognition
- ONNX Accuracy: 100%
- RKNN Frame Rate: 3.64 FPS
- Function Verification: Offline evaluation, real-time capture, 1:N recognition mode
Why Edge Deployment Matters
Traditional iris recognition systems rely on server-side processing, which introduces latency, requires network connectivity, and raises data privacy concerns. Edge deployment solves all three issues:
- Zero Latency: Recognition happens directly on the device, enabling sub-second response times
- Offline Capability: Systems continue to function without network connectivity
- Data Privacy: Biometric data never leaves the device, satisfying strict data protection regulations
- Cost Reduction: Eliminates the need for expensive server infrastructure
The Domestic NPU Advantage
By deploying on the domestically produced RK3588 platform, Homsh ensures supply chain independence and supports China's technology self-sufficiency goals. The RK3588's 6 TOPS NPU computing power provides sufficient capacity for real-time multi-modal biometric recognition.
Applications
This edge-deployed model is ideal for:
- Access control terminals in areas with limited connectivity
- Mobile and handheld biometric devices
- Smart locks and IoT security devices
- Border control checkpoints requiring rapid processing
