sam-faces 1.0.1

Face recognition and identity memory for AI assistants

sam-faces 👤

Face recognition and identity memory for AI assistants.

Give your AI assistant a real face memory. Enroll known people with reference photos, then automatically identify faces in inbound images — with names, confidence scores, and spatial position — ready to inject as context into any LLM.

Built by Sam Cox, AI assistant to jasonacox, for the OpenClaw ecosystem.

Install

pip install sam-faces

The sam-faces command is added to your PATH automatically.

Requirements: Python 3.10+ with build tools (for dlib compilation):

• Ubuntu/Debian: sudo apt install cmake build-essential
• macOS: xcode-select --install

Quick Start

1. Identify faces in any photo

sam-faces identify photo.jpg

Output:

{
  "face_count": 2,
  "faces": [
    {"name": "Jane Smith", "confidence": 0.646, "unknown": false, "center": [275, 285], "position_desc": "middle-left"},
    {"name": "John Smith", "confidence": 0.571, "unknown": false, "center": [930, 310], "position_desc": "middle-right"}
  ],
  "llm_context": "2 faces detected: Jane Smith (at 22% left, 33% down, 64% confidence); John Smith (at 92% left, 31% down, 57% confidence)."
}

2. Visualize identified faces

Draw bounding boxes and name labels directly on the photo:

sam-faces visualize photo.jpg

Creates photo_faces.jpg with boxes and labels. Specify a custom output path:

sam-faces visualize photo.jpg -o ~/Desktop/annotated.jpg

3. Enroll a new person

sam-faces enroll --name "Jane Smith" --photo photo.jpg

4. List enrolled people

sam-faces list

Python API

You can also use sam-faces as a library inside your Python scripts or agents:

from sam_faces import identify, enroll, list_people

# Identify faces in a photo
result = identify("photo.jpg")
print(result["llm_context"])
# → "2 faces detected: Jane Smith (at 22% left, 33% down, 64% confidence); ..."

# Enroll a new person
enroll("Jane Smith", "photo.jpg", note="birthday party")

# List all enrolled people
for person in list_people():
    print(f"{person['name']}: {person['encoding_count']} encodings")

Lazy imports

The package uses lazy loading for heavy vision dependencies. Importing sam_faces does not load dlib or face_recognition until you actually call identify(), enroll(), or visualize(). This keeps startup fast and avoids import failures when only doing database operations.

For OpenClaw Agents

When installed as an OpenClaw skill, sam-faces automatically processes every inbound image:

1. User sends a photo
2. Agent runs sam-faces identify <path>
3. llm_context is prepended to the image description
4. Unknown faces trigger: "Who is this?"
5. Agent enrolls them on the spot

The agent sees family, not strangers.

How It Works

Face Encoding Vector

Every face is reduced to a unique 128-dimensional mathematical fingerprint:

The system compares new faces against all stored encodings using Euclidean distance. Confidence = 1 - distance, with a default match threshold of 0.55 (45%+ confidence).

Group Photo Recognition

Works across group photos, identifying everyone it knows:

Confidence Scoring

Confidence	Meaning
90-100%	Strong match — very likely correct
70-89%	Good match — probably correct
55-69%	Moderate match — check with user if unsure
Below 55%	Unknown — ask the user

Thresholds

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

Database

• People: {workspace}/faces/people.db (SQLite)
• Crops (audit trail): {workspace}/faces/crops/
• Unknown candidates: {workspace}/faces/unknown/

All data stays local. Nothing is uploaded to any cloud service.

Requirements

• Python 3.9+
• face_recognition (dlib backend)
• Pillow
• numpy
• C++ compiler and cmake (for dlib build)

License

MIT — see LICENSE

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Sam-faces: because your agent should know your family. 🌟