sam-faces 0.1.0

Face recognition and people memory for AI assistants

sam-faces 👤

Face recognition and people 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 bounding box coordinates — ready to inject as context into any LLM.

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

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How It Works

Step 1 — Detect & Identify

Feed any photo and get back labeled bounding boxes with confidence scores:

Step 2 — Multi-Person Recognition

Works across group photos, identifying everyone it knows:

Step 3 — The Face Encoding Vector

Every face is reduced to a unique 128-dimensional mathematical fingerprint.
No two people produce the same pattern — this is what makes identification possible:

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).

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Features

• 🧠 SQLite people database — scales from a handful of family members to thousands of faces
• 📸 Multi-encoding per person — enroll multiple photos per person for better accuracy across angles, lighting, and years
• 🔍 Structured JSON output — bounding boxes, confidence scores, position descriptions, and an llm_context string ready to pass to any LLM
• 👤 Unknown candidate tracking — unrecognized faces are saved with cropped images for later enrollment
• 🔒 100% local — no cloud APIs, no data leaves your machine
• 🤖 LLM-ready — output designed to enrich image analysis with identity context

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Installation

# Install system dependencies (Ubuntu/Debian)
sudo apt-get install -y cmake python3-dev

# Install Python packages
pip install face_recognition pillow numpy

Note: face_recognition compiles dlib from source. This takes 5–10 minutes on first install. Be patient.

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Quick Start

1. Enroll a person

python -m sam_faces.enroll_face --name "Jane Smith" --photo jane.jpg --note "Office headshot 2026"

If multiple faces are detected, you'll be prompted to choose which one to enroll.

2. Identify faces in a photo

python -m sam_faces.identify_faces --photo group_photo.jpg

Output:

{
  "face_count": 2,
  "faces": [
    {
      "name": "Jane Smith",
      "confidence": 0.94,
      "unknown": false,
      "bounding_box": {"top": 120, "right": 340, "bottom": 280, "left": 180},
      "center": [260, 200],
      "position_desc": "upper-left"
    },
    {
      "name": "Unknown",
      "confidence": null,
      "unknown": true,
      "unknown_id": "a1b2c3d4",
      "bounding_box": {"top": 80, "right": 600, "bottom": 240, "left": 450},
      "center": [525, 160],
      "position_desc": "upper-right"
    }
  ],
  "llm_context": "2 faces detected: Jane Smith (upper-left, 94% confidence); Unknown person (upper-right)."
}

3. Use llm_context with your LLM

Pass the llm_context string alongside the image to any vision model:

from sam_faces.identify_faces import identify

result = identify("photo.jpg")
prompt = f"Describe this image. People identified: {result['llm_context']}"
# → "Describe this image. People identified: 2 faces detected: Jane Smith (upper-left, 94% confidence); Unknown person (upper-right)."

4. List enrolled people

python -m sam_faces.face_db --list

5. Review unknown faces

python -m sam_faces.face_db --unknowns

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Python API

from sam_faces.identify_faces import identify
from sam_faces.face_db import init_db, add_person, add_encoding, list_people
import face_recognition

# Initialize DB
init_db()

# Enroll programmatically
image = face_recognition.load_image_file("photo.jpg")
encodings = face_recognition.face_encodings(image)
person_id = add_person("Jane Smith")
add_encoding(person_id, encodings[0], note="Office 2026")

# Identify
result = identify("group_photo.jpg")
print(result["llm_context"])

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Configuration

Set the SAM_FACES_DB environment variable to use a custom database location:

export SAM_FACES_DB=/path/to/your/people.db

Default: ./faces/people.db relative to the package root.

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Database Schema

people(id TEXT, name TEXT, created_at TEXT)
encodings(id TEXT, person_id TEXT, vector BLOB, note TEXT, added_at TEXT)
unknown_candidates(id TEXT, image_path TEXT, face_crop_path TEXT,
                   detected_at TEXT, resolved INTEGER, resolved_as TEXT)

Vectors are stored as raw float64 binary blobs (128 dimensions from dlib's face encoding model).

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Multiple Encodings Per Person

Enroll the same person from multiple photos to improve accuracy:

python -m sam_faces.enroll_face --name "Jane Smith" --photo jane_2020.jpg --note "2020 — longer hair"
python -m sam_faces.enroll_face --name "Jane Smith" --photo jane_2026.jpg --note "2026 — current"

The system matches against all encodings for a person and uses the best score. This handles aging, hairstyle changes, glasses, and different lighting conditions.

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Unknown Face Pipeline

When an unrecognized face is detected:

1. It's saved to unknown_candidates in the database
2. A cropped face image is saved to faces/unknown/
3. Later, you can enroll it: python -m sam_faces.enroll_face --name "Bob" --photo faces/unknown/unknown_photo_120_80.jpg

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Matching Threshold

Default threshold: 0.55 (distance). Lower = stricter matching.

# Stricter (fewer false positives)
python -m sam_faces.identify_faces --photo photo.jpg --threshold 0.45

# More lenient (better recall for difficult angles)
python -m sam_faces.identify_faces --photo photo.jpg --threshold 0.65

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Privacy

• All face data stays 100% local — no API calls, no cloud uploads
• The database contains only face encodings (128-dimensional vectors), not raw photos
• Add faces/people.db and faces/unknown/ to your .gitignore

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License

MIT License — Copyright (c) 2026 Sam Cox

See LICENSE for details.

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Acknowledgements

Built on top of Adam Geitgey's face_recognition library and dlib by Davis King.

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Part of the OpenClaw AI assistant ecosystem.