Welcome to the Face Verification and Recognition repository. This project uses deep learning models and a Triplet loss function to calculate the error and image encodings for face verification and recognition.
The goal of this project is to develop an algorithm that can verify a person's identity using a face image. The project uses a deep learning model to generate encodings of face images, and a Triplet loss function to calculate the error between these encodings. The model can then compare the encodings of two face images to verify whether they are of the same person.
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Main.ipynb: This Jupyter notebook contains the main code for the project. It includes the steps for loading and preprocessing the data, creating and training the model, and making predictions.
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fr_utils.py: This Python file contains utility functions used in the project.
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inception_blocks_v2.py: This Python file contains the code for the Inception model used in the project.
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nn4.small2.v7.h5: This file contains the pre-trained weights for the model.
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datasets: This directory contains the datasets used in the project.
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images: This directory contains the images used in the project.
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weights: This directory contains the weights for the model.
To get started with this project, clone the repository and install the required Python packages. You can then run the Main.ipynb notebook to train the model and make predictions.
Please note that this project is a work in progress, and the model's predictions should not be used for actual face verification without further testing and validation.