Face-Recognition-Using-Siamese-Network

Face Recognition Using Siamese Network

A deep learning project that implements face recognition using Siamese Neural Networks with contrastive loss. This project can identify and match faces from images by learning robust face embeddings.

Overview

This project implements a face recognition system using Siamese Neural Networks, which are particularly effective for one-shot learning tasks. The system learns to generate embeddings for face images and can identify whether two face images belong to the same person or different people.

Architecture

The project uses a Siamese Network architecture with the following components:

Siamese Network Structure:

Two identical neural networks that share weights

Each network processes one input image

Outputs face embeddings for comparison

Contrastive Loss:

Measures the similarity between pairs of face embeddings

Minimizes distance for same-person pairs

Maximizes distance for different-person pairs

Key Features

Face detection and cropping using MTCNN

Siamese Network for face embedding generation

Contrastive loss for training

Face database management

Real-time face matching