Add Face Verification

The Goal of the project is to do Face recognition task using Siamese Neural Network that is trained over a triplet loss function , This is useful in situations where you have limited dataset for training the model.

This codebase implements a face recognition system using a Siamese Neural Network architecture. It helps you understand how this approach works and tackles the challenge of large datasets by employing one-shot learning techniques.

Model Functionality:

Feature Extraction: Generates a 128-dimensional vector embedding for any input image, capturing its key features.
Similarity Measurement: Compares the 128-dimensional vectors of two images to determine their similarity and classify them as belonging to the same person or not.
Triplet Loss for Training:

The model leverages a triplet loss function during training. This function uses triplets of images: anchor (reference), positive (same person), and negative (different person). It aims to:

Minimize the distance between the anchor and positive embeddings.
Maximize the distance between the anchor and negative embeddings.
This approach helps the model learn robust representations for effective face discrimination.