FaceNet: A Unified Embedding for Face Recognition and Clustering
Paper • 1503.03832 • Published
FaceNet is a deep metric learning framework for face recognition and verification that maps face images into a compact embedding space where distances directly correspond to face similarity.
Original paper: FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet
This model uses FaceNet with a MobileNetV1 backbone, providing a lightweight, efficient architecture suitable for real-time face recognition on mobile and embedded devices. It is well suited for applications such as authentication, access control, and edge-based identity verification.
Model Configuration:
- Reference implementation: FaceNet MobileNetV1 implementation
- Original Weight: FaceNet MobileNetV1 weight Extraction code: anv6
- Resolution: 3x160x160
- Support Cooper version:
- Cooper SDK: [2.5.3]
- Cooper Foundry: [2.2]
| Model | Device | compression | Model Link |
|---|---|---|---|
| FaceNet | N1-655 | Amba_optimized | Model_Link |
| FaceNet | CV7 | Amba_optimized | Model_Link |
| FaceNet | CV72 | Amba_optimized | Model_Link |
| FaceNet | CV75 | Amba_optimized | Model_Link |
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