Metadata-Version: 2.4
Name: faceverifyai
Version: 0.1.0
Summary: Siamese CNN based face verification package
License-Expression: MIT
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: tensorflow>=2.15
Requires-Dist: numpy
Requires-Dist: pillow
Dynamic: license-file

# FaceVerifyAI 

[![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/downloads/)
[![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange.svg)](https://www.tensorflow.org/)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abhisekkundu-DS/siamese-face-verification/blob/main/demo.ipynb)

A Siamese CNN-based face verification Python package built with TensorFlow/Keras for checking whether two face images belong to the same person.

This package provides a simple API for face similarity verification using a pretrained deep learning model with a custom L2Normalize layer.

## ✨ Features

-  Verify whether two face images match
-  Siamese CNN architecture for similarity learning
-  Easy Python API (`verify_face`)
-  Installable as a reusable Python package
-  Tested on local machine + Google Colab
-  Includes custom TensorFlow layer support
-  Automatic image preprocessing




## ⚙️ Installation

### Install from GitHub

```bash
git clone https://github.com/abhisekkundu-DS/siamese-face-verification.git
cd siamese-face-verification
pip install -e .

pip install git+https://github.com/abhisekkundu-DS/siamese-face-verification.git
```
### Create a virtual environment
```bash
python -m venv tfenv
Windows PowerShell:

powershell
.\tfenv\Scripts\Activate
Linux/Mac:

bash
source tfenv/bin/activate
 Install the package
bash
pip install -e .

```
### Example :
```
Create a test file
Create test.py in the root folder:

python
from faceverifyai import verify_face

result = verify_face("person1.jpg", "person2.jpg")
print(result)
```
```
Run the test
bash
python test.py
 Expected output
python
{'match': True, 'score': 0.94}
```
