A deep learning face verification system built on a Siamese network architecture. Rather than classifying faces into fixed identity classes, the system learns a similarity metric in embedding space enabling verification of new identities without retraining.
The pipeline covers the full lifecycle: data collection, preprocessing, Siamese network training, and real-time face verification.
The key insight: face verification is a similarity problem, not a classification problem.
Why Siamese Networks over standard classifiers?
A standard classifier requires retraining when new identities are added. A Siamese network learns a general similarity metric once trained, it can verify any two face images without modification. This makes it practical for real-world deployment where the identity set is dynamic.
Image A ──► CNN Encoder ──► Embedding A ──┐
├──► Distance ──► Similar / Different
Image B ──► CNN Encoder ──► Embedding B ──┘
(shared weights)
The two branches share identical weights, this is the defining property of the Siamese architecture. Training uses contrastive loss to pull embeddings of the same identity together and push different identities apart.
| Stage | Description |
|---|---|
| Data Collection | Face image dataset assembly and augmentation |
| Preprocessing | Face detection, alignment, normalization |
| Training | Siamese network with contrastive loss |
| Evaluation | Verification accuracy, FAR/FRR metrics |
| Real-Time Inference | Live face verification pipeline |
| Layer | Technology |
|---|---|
| Deep Learning | PyTorch |
| Architecture | Siamese Network with shared CNN encoder |
| Loss Function | Contrastive Loss |
| Computer Vision | OpenCV |
| Language | Python 3.10+ |
git clone https://github.com/royxlead/deep-learning-facial-recognition-python.git
cd deep-learning-facial-recognition-python
pip install -r requirements.txt
# Collect training data
python collect_data.py
# Train the Siamese network
python train.py
# Run real-time verification
python verify.pySiamese networks were introduced for signature verification and have since become a foundational architecture for one-shot learning and metric learning problems. This implementation applies the architecture to face verification, exploring the trade-off between embedding dimensionality, contrastive margin, and verification accuracy on a custom dataset.
- Multi-Objective Feature Selection - Evolutionary optimization for feature engineering
- Self-Diagnosing Neural Models - Uncertainty estimation in deep learning