Advanced-Signature-Recognition-System-using-Few-Shot-Learning-and-Siamese-Networks

Project Description

This project implements a Siamese Network for signature verification, leveraging few-shot learning to perform effective feature extraction and signature matching with minimal training images per class. The network learns to compare signature pairs and determine whether they belong to the same individual. A K-Nearest Neighbors (KNN) classifier is used to enhance signature classification accuracy.

Key Features:

• Few-shot learning: Efficient signature classification with limited data.
• Siamese Network: Learns feature embeddings for signature similarity comparison.
• KNN Classifier: Uses learned feature representations for final classification.
• Automated dataset setup: Automatically downloads dataset if not available.

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Project Structure

.
├── Signature_few_shot_learning.ipynb    # Jupyter Notebook with code implementation
└── Dataset                               # Dataset directory
    ├── class1                           # Signature images for class 1
    └── class2                           # Signature images for class 2

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About the Dataset

The dataset consists of signature images grouped into different classes. Each class represents signatures from a single individual. The model is trained on signature pairs to learn similarities and differences. The dataset follows the structure:

Dataset/
├── class1/
│   ├── signature1.png
│   ├── signature2.png
│   └── ...
├── class2/
│   ├── signature1.png
│   ├── signature2.png
│   └── ...

If the dataset is not available, the script will automatically download it.

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Implementation Steps

1. Install Dependencies

Ensure Python 3.10 is installed and dependencies are available.

Run the following commands:

# Check if pip is installed
pip --version

# Upgrade pip (if needed)
python3.10 -m pip install --upgrade pip

# Install required dependencies
pip install -r requirements.txt

2. Set Up the Dataset

Ensure the dataset is structured as mentioned. If not available, it will be automatically downloaded.

3. Run the Jupyter Notebook

Execute the Jupyter Notebook to train and evaluate the Siamese Network.

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Model Architecture: Siamese Network

What is Signature Verification?

Signature verification is a biometric authentication technique that determines whether a given signature matches a known reference signature.

How Does the Siamese Network Work?

A Siamese Network consists of two identical sub-networks that learn feature representations from input images. Given two signature images, the network computes their embeddings and measures similarity.

Steps:

1. Two signature images are passed through identical CNN feature extractors.
2. The output embeddings are compared using a distance metric (e.g., Euclidean distance).
3. If the distance is below a threshold, the signatures are classified as a match; otherwise, they are considered different.

Key Components:

• Convolutional Neural Network (CNN): Extracts signature features.
• Contrastive Loss Function: Optimizes embedding distance for similarity learning.
• K-Nearest Neighbors (KNN): Uses extracted features for final classification.

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Loss Functions

1. Contrastive Loss

Encourages similar signatures to have closer embeddings while pushing different ones apart.

2. Binary Cross-Entropy Loss

Used when treating signature verification as a binary classification problem (match vs. non-match).

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Conclusion

This project demonstrates how Siamese Networks can be effectively used for signature verification with few-shot learning. The model learns to recognize signature similarities with minimal labeled data, making it useful for real-world authentication systems.

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Future Improvements

• Experimenting with Triplet Loss for improved similarity learning.
• Fine-tuning with a larger dataset for better generalization.
• Implementing real-time signature verification using a deployed model.

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References

• Original Siamese Network Paper
• Few-Shot Learning Techniques

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Author: Sai Krishna Chowdary Chundru

GitHub: github.com/sAI-2025
LinkedIn: linkedin.com/in/sai-krishna-chowdary-chundru