face_id 0.2.0

Face detection (bounding boxes, eyes/nose/mouth markers), and facial recognition (embeddings).
Documentation

Face ID

Face detection & recognition crate.

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Face detection

This crate uses SCRDF face detection models. The following models are available:

The naming convention for the ONNX models indicates the computational complexity (measured in FLOPs) and whether the model includes 5 facial keypoints predictions in addition to standard bounding boxes.

Name Easy Medium Hard FLOPs Params(M) Infer(ms) BBox Facial Keypoints
500m.onnx 90.57 88.12 68.51 500M 0.57 3.6 ✅ ❌
1g.onnx 92.38 90.57 74.80 1G 0.64 4.1 ✅ ❌
34g.onnx 96.06 94.92 85.29 34G 9.80 11.7 ✅ ❌
2.5g_bnkps.onnx 93.80 92.02 77.13 2.5G 0.82 4.3 ✅ ✅
10g_bnkps.onnx 95.40 94.01 82.80 10G 4.23 5.0 ✅ ✅
34g_gnkps.onnx ? ? ? 34G ? ? ✅ ✅

Keypoints (kps) and Normalization Types (bn vs gn)

  • kps: Denotes models that output 5 facial landmarks (keypoints) in addition to the standard bounding boxes.
  • bnkps: Models trained using Batch Normalization (BN). These often have lower false-positive rates and high recall on general datasets. However, they occasionally struggle with producing accurate landmarks for faces that are rotated past 90 degrees or are unusually large.
  • gnkps: Models trained using Group Normalization (GN). These variants (e.g., 34g_gnkps or 10g_gnkps) were explicitly developed to fix issues with very large faces that the bnkps models exhibited. While they improve landmark quality on large or rotated faces, they might have slightly lower general recall than bnkps.

Face recognition (embeddings)

Use ArcFace ... explain more here. it needs to get aligned faces where the mouth, nose, eyes are in exact spots in an 112x112 image. this is handled by this crate.

you can use hf model id: public-data/insightface with filename "models/buffalo_l/w600k_r50.onnx" to embed faces.