edgeface-knn 2.0.2

Real-time CPU face recognition pipeline using classical ML, refactored from an embedded Raspberry Pi deployment into an installable modular package.

Edge Face Recognition (CPU-Only)

Real-time face recognition designed for CPU-only environments (laptops, embedded devices, Raspberry Pi).

A classical computer-vision pipeline (Haar Cascade + KNN) delivering ~40 ms inference latency without GPUs or deep-learning frameworks. Intended for offline attendance and privacy-sensitive deployments where cloud inference is not viable.

Install: pip install edgeface-knn
Package name: edgeface-knn (PyPI) | Repository: edge-face-recognition-v2 (GitHub)

Performance: ~40 ms per processed frame (~15 FPS effective)

---

Problem Context

A lightweight identity recognition system intended for:

• Attendance systems
• Lab / hostel / office access logging
• Offline environments
• Edge devices with no GPU
• Privacy-sensitive deployments (no cloud inference)

The system prioritizes correct identification over aggressive guessing — unknown faces are rejected instead of force-matched.

---

Who is this for?

• Want to use it → follow Quick Install
• Want to modify it → follow Development Setup

---

Installation

Quick Install (Recommended)

Camera capture requires native OS execution (WSL users see section below).

pip install edgeface-knn
edge-face --help
# Expected: shows collect/run commands

Development Setup

git clone https://github.com/SakshamBjj/edge-face-recognition-v2.git
cd edge-face-recognition-v2
pip install -e .

Originally built as a Raspberry Pi prototype (Sept 2024).
Refactored into a modular installable Python package (Dec 2025).

---

Usage

1) Register people

edge-face collect --name Alice
edge-face collect --name Bob

Captures 100 samples per person via webcam automatically.

---

2) Run recognition

edge-face run

Controls:

Key	Action
o	Log attendance
q	Quit

Logs saved to:

attendance/YYYY-MM-DD.csv

---

3) Optional configuration

Override default parameters:

edge-face run --config configs/my_config.yaml

---

WSL Development Notes

Important: Webcam access requires native OS execution. This limitation comes from WSL hardware virtualization — not the library.

Development workflow:

Task	Environment
Code editing / packaging	WSL
Face collection	Windows (native)
Real-time recognition	Windows (native)

Testing from WSL:

# In WSL: Install editable package
pip install -e .

# In Windows terminal (same project directory):
edge-face collect --name TestUser
edge-face run

The package itself is OS-independent, but webcam access requires native execution.

---

Runtime Pipeline

Camera (30 FPS)
 → Grayscale conversion
 → Haar Cascade detection
 → Crop + resize (50×50)
 → Flatten vector
 → KNN classification
 → Confidence scoring
 → Unknown rejection
 → Overlay + logging

Frame skipping processes every 2nd frame to maintain smooth real-time UX.

---

Unknown Face Handling

The system favors precision over recall.

Instead of always predicting a nearest neighbor:

Confidence	Result
≥ threshold	Person identified
< threshold	Marked "Unknown"

Prevents the most serious failure in face recognition systems: logging the wrong person.

---

Model Selection Rationale

Factor	This Project (KNN)	CNN Face Recognition
Model size	<1 MB	~90 MB
CPU inference	~40 ms	~300 ms
GPU required	No	Yes
Training data	~100 samples/person	1000+ samples/person

Design goal: predictable latency on CPU hardware — not maximum accuracy on servers.

Deep learning was prototyped but exceeded real-time limits without GPU acceleration.

---

Performance

Accuracy (typical indoor lighting)

Condition	Accuracy
Frontal face	~95%
Glasses	~90%
Mask	~75%
±30° angle	~70%

Latency

Stage	Time
Detection	20 ms
Preprocess	5 ms
Classification	15 ms
Total	~40 ms

---

Known Limitations

1. Low lighting reduces detection reliability
2. Side profiles (>30°) often not detected
3. Performance degrades beyond ~100 identities (KNN O(n))
4. Not spoof-proof (photo attacks possible)

---

Engineering Tradeoffs

Decision	Reason	Cost
Haar Cascade	20 ms detection	Angle robustness
Raw pixels	No feature extraction overhead	Less compact representation
KNN	No training step	Scaling limits
Frame skipping	Real-time UX	Slight temporal jitter
Unknown rejection	Avoid false positives	Occasional false negatives

---

Package Layout

edge-face-recognition-v2/
├── src/edge_face/
├── ├── default.yaml
│   ├── __init__.py
│   ├── cli.py
│   ├── config.py
│   ├── detector.py
│   ├── dataset.py
│   ├── model.py
│   └── pipeline.py
├── scripts/collect_faces.py
├── data/              (generated)
└── attendance/        (generated)

---

Project Evolution

Version	Focus
v1	Embedded Raspberry Pi prototype
v2	Installable reusable software package

Archived prototype available in repository history.

---

What this demonstrates

• Designing ML systems under hardware constraints
• Latency-driven model selection
• Converting prototype code into a distributable tool
• Building CLI-driven reproducible workflows

---

References

• Viola-Jones Face Detection
• OpenCV face recognition documentation
• scikit-learn KNN implementation

---

Author: Saksham Bajaj
License: MIT