Face_Recognition

🧠 Face Recognition System (ArcFace + YOLOv8)

📖 Overview

This repository hosts a production-ready Face Recognition Pipeline designed for high-accuracy biometric identification. Unlike standard recognizers, this system integrates YOLOv8 for robust face detection and alignment before feature extraction.

The core recognition model is built upon a Wide ResNet-101-2 backbone, trained with a hybrid loss function (ArcFace + Center Loss) to generate highly discriminative 512-dimensional embeddings.

🌟 Key Features

• Robust Detection: Uses YOLOv8 (ONNX) to detect faces even in challenging lighting or angles.
• High Accuracy: Achieves 90.5% accuracy on the LFW (Labeled Faces in the Wild) dataset and 90% on Validation.
• Discriminative Embeddings: 512-dim vectors optimized for Cosine Similarity.
• Easy-to-Use API: Includes a wrapper (inference.py) for 3-line code implementation.
• Fine-tuning Ready: Includes scripts to retrain the model on your custom dataset.

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🛠️ Installation

To run the pipeline, you need to install the necessary dependencies. We recommend using a virtual environment.

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118  # For CUDA support
pip install opencv-python onnxruntime-gpu huggingface_hub pillow tqdm numpy

Step 1: Download the Wrapper

• Download our helper script inference.py which handles model downloading and YOLO detection automatically.

wget https://huggingface.co/biometric-ai-lab/Face_Recognition/resolve/main/inference.py

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Step 2: Create & Run Python Script

• Create a new file named run_demo.py.
• Copy and paste the code below into it.
• Make sure you have 2 images to test (e.g., face1.jpg and face2.jpg).

# File: run_demo.py
from inference import FaceAnalysis

# 1. Initialize the AI (Downloads models automatically on first run)
print("⏳ Initializing models...")
app = FaceAnalysis()

# 2. Define your images
img1_path = "face1.jpg"  # <--- Change this to your image path
img2_path = "face2.jpg"  # <--- Change this to your image path

# 3. Run Comparison
print(f"🔍 Comparing {img1_path} vs {img2_path}...")

try:
    # Get similarity score and boolean result
    similarity, is_same = app.compare(img1_path, img2_path)

    print("-" * 30)
    print(f"🔹 Similarity Score: {similarity:.4f}")
    print("-" * 30)

    if is_same:
        print("✅ RESULT: SAME PERSON")
    else:
        print("❌ RESULT: DIFFERENT PERSON")

except Exception as e:
    print(f"Error: {e}")
    print("Tip: Make sure the image paths are correct!")

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🎓 Training Guide

Option: Full Training (Advanced): Use train.py to train the model from scratch (ImageNet weights) on a large dataset. Step 1: Prepare Dataset

• Organize images in ImageFolder format

dataset/
├── person_1/
│   ├── img1.jpg
│   └── ...
└── person_2/
    └── img1.jpg

Step 2: Run Training

python train.py \\
    --data_dir ./dataset \\
    --output_dir ./checkpoints \\
    --epochs 50 \\
    --batch_size 64 \\
    --lr_backbone 8e-6 \\
    --lr_head 8e-5

📘 About This Project

This project is developed by a group of undergraduate students
from Ho Chi Minh City University of Technology and Education (HCMUTE),
Cohort K23, as part of academic research and learning activities.