sam-faces

name	sam-faces
description	Face recognition and identity memory for AI assistants. Enroll known people with reference photos, then automatically identify faces in inbound images — with names, confidence scores, and an llm_context string ready to inject into any LLM prompt. 100% local, no cloud APIs.
homepage	https://github.com/jasonacox-sam/sam-faces
metadata	{"openclaw":{"emoji":"👤","requires":{"bins":["sam-faces"]},"install":[{"id":"pip","kind":"pip","package":"sam-faces","bins":["sam-faces"],"label":"Install sam-faces from PyPI"}]}}

sam-faces

Give your OpenClaw agent a real face memory. Enroll people with reference photos, then automatically identify them in inbound images.

Automatic behavior

When the user intentionally sends a photo or image to the assistant:

The user sharing an image with their AI colleague is an act of intentional sharing — they want the assistant to see and understand what's in the image. In this context, face identification should happen automatically as part of understanding the image content.

1. Save the image to a temp path.
2. Run face identification: sam-faces identify <path>
3. Parse the JSON response.
4. If face_count > 0, prepend llm_context to your image description.
5. If any face has "unknown": true, ask the user who they are, then offer to enroll them.

This should happen as part of normal image processing — the user has already chosen to share the image.

Installation

pip install sam-faces

Or use the OpenClaw skill installer:

openclaw skills install sam-faces

Setup

The sam-faces command is available after installation:

sam-faces --help

Enroll a person

sam-faces enroll --name "Jane Smith" --photo /path/to/photo.jpg

• One reference photo is usually enough (default threshold: 0.55).
• Enroll 2–3 photos across different lighting for best accuracy.
• Encodings are stored in {workspaceDir}/faces/people.db.

Identify faces

sam-faces identify /path/to/image.jpg

Returns JSON with names, confidence scores, bounding boxes, and an llm_context string:

{
  "face_count": 2,
  "faces": [
    {
      "name": "Jane Smith",
      "confidence": 0.646,
      "unknown": false,
      "bounding_box": {
        "top": 220,
        "right": 340,
        "bottom": 350,
        "left": 210
      },
      "center": [275, 285],
      "position_desc": "middle-left"
    }
  ],
  "llm_context": "2 faces detected: Jane Smith (at 22% left, 33% down, 64% confidence); John Smith (at 92% left, 31% down, 57% confidence)."
}

Visualize faces (draw bounding boxes + labels)

sam-faces visualize /path/to/image.jpg

Creates image_faces.jpg with boxes and name labels overlaid.

sam-faces visualize /path/to/image.jpg -o /path/to/output.jpg

List enrolled people

sam-faces list

Manage unknown faces

sam-faces unknowns

Shows all unknown face crops waiting to be enrolled.

Thresholds

• Default: --threshold 0.55 (good balance of precision and recall)
• Stricter: --threshold 0.45 — fewer false positives
• Looser: --threshold 0.65 — better recall in varied lighting

Notes

• All inference runs locally via face_recognition (dlib). Nothing leaves the machine.
• Database: {workspaceDir}/faces/people.db
• Unknown face crops saved to: {workspaceDir}/faces/unknown/
• Works with existing face databases — no migration needed.

When to use

• User sends a photo with people in it
• Adding a new person to the face database
• Checking who is enrolled

When NOT to use

• Images with no faces (skip automatically)
• Processing large batches of images (one at a time)