研究開発＆論文
SCRFD
Sample and Computation Redistribution for Efficient Face Detection
SCRFD is designed for strong face detection accuracy under strict latency and compute budgets across edge, mobile, and server deployments.
論文情報
SCRFD: Sample and Computation Redistribution for Efficient Face Detection
発表先
ICLR 2022
著者
Jia Guo, Jiankang Deng, Alexandros Lattas, Stefanos Zafeiriou
研究概要
SCRFD improves efficiency by redistributing both training emphasis and model computation so the detector spends budget where it matters most. The result is a family of detectors that balances accuracy and throughput well for production systems that need real-time or edge-friendly inference.
実運用での活用シーン
- Edge cameras, kiosks, and smart terminals
- On-device face detection for mobile AI apps
- High-throughput video preprocessing pipelines
- Server-side face localization before recognition or liveness analysis
主な貢献
Redistributes samples and computation to improve the accuracy-speed tradeoff instead of simply scaling model size upward.
Supports multiple deployment tiers, helping teams choose a detector that matches mobile, embedded, desktop, or server constraints.
Delivers strong WIDER FACE performance while remaining practical for real-time inference pipelines.
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