# InsightFace — Open-Source 2D and 3D Face Analysis Toolkit

> InsightFace provides state-of-the-art face detection, recognition, alignment, and attribute analysis models including ArcFace and RetinaFace, with support for PyTorch, MXNet, and ONNX Runtime deployment.

## Install

Save as a script file and run:

# InsightFace — Open-Source 2D and 3D Face Analysis Toolkit

## Quick Use
```bash
pip install insightface onnxruntime
python -c "
import insightface
from insightface.app import FaceAnalysis
app = FaceAnalysis(providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))
import cv2
img = cv2.imread('photo.jpg')
faces = app.get(img)
print(f'Found {len(faces)} faces')
"
```

## Introduction
InsightFace is an open-source toolkit for 2D and 3D face analysis research and deployment. It bundles high-accuracy models for detection, recognition, alignment, age and gender estimation, and 3D face reconstruction under a unified Python API.

## What InsightFace Does
- Detects faces in images and video using SCRFD and RetinaFace detectors
- Generates face embeddings with ArcFace for accurate identity matching
- Estimates facial landmarks for alignment and pose correction
- Predicts age, gender, and facial attributes from a single frame
- Reconstructs 3D face meshes from 2D photographs

## Architecture Overview
InsightFace organizes models into a model zoo with ONNX exports for portable inference. The FaceAnalysis pipeline chains a detector, landmark predictor, and recognition model. Training scripts use PyTorch or MXNet with configurable loss functions like ArcFace, CosFace, and SubCenter variants, while deployment relies on ONNX Runtime for cross-platform speed.

## Self-Hosting & Configuration
- Install via pip; GPU inference requires onnxruntime-gpu and CUDA drivers
- Download model packs from the official model zoo or Hugging Face
- Set det_size and det_thresh to balance speed against detection recall
- Use the recognition module standalone for embedding-only workflows
- Deploy as an HTTP service by wrapping FaceAnalysis in FastAPI or Flask

## Key Features
- ArcFace recognition achieves over 99.8% accuracy on LFW benchmark
- SCRFD detector runs at 10 ms per frame on a modern GPU
- Supports ONNX export for deployment on edge devices and mobile
- Includes 3D face reconstruction with dense landmark prediction
- Model zoo covers detection, recognition, age-gender, and anti-spoofing

## Comparison with Similar Tools
- **DeepFace** — higher-level wrapper with multiple backends; InsightFace provides the actual trained models
- **dlib** — classic HOG and CNN detectors; InsightFace offers newer architectures with better accuracy
- **MediaPipe Face** — optimized for real-time mobile; InsightFace targets higher accuracy research use
- **FaceNet** — single recognition model; InsightFace bundles detection, alignment, and recognition together
- **OpenCV DNN** — general-purpose inference; InsightFace is purpose-built for face analysis tasks

## FAQ
**Q: Does InsightFace require a GPU?**
A: No. ONNX Runtime with the CPU provider works out of the box, though a GPU significantly speeds up batch processing.

**Q: Can I use InsightFace for commercial projects?**
A: The code is MIT-licensed. Some pretrained models have separate licenses, so check the model zoo page for each model.

**Q: How do I compare two face embeddings?**
A: Compute the cosine similarity between the two 512-d vectors returned by the recognition model. A threshold of 0.4-0.5 works for most applications.

**Q: Does it support video streams?**
A: Yes. Call app.get() on each frame from OpenCV's VideoCapture. For real-time use, reduce det_size to lower latency.

## Sources
- https://github.com/deepinsight/insightface
- https://insightface.ai/

---
Source: https://tokrepo.com/en/workflows/asset-269eb56d
Author: Script Depot