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Face Identification using Deep Learning

Overview

This project aims to implement face identification using deep learning techniques. The model is trained to recognize and identify faces in images, making it suitable for applications such as facial recognition systems, security systems, and more.

Technology Stack

1. Deep Learning Framework

  • TensorFlow: The project utilizes the TensorFlow deep learning framework for building, training, and deploying the face identification model.

2. Neural Network Architecture

  • Convolutional Neural Network (CNN): The core of the model is a CNN, which is well-suited for image recognition tasks. It is designed to learn hierarchical representations of facial features.

3. Data Preprocessing

  • OpenCV: OpenCV is used for image preprocessing tasks such as resizing, normalization, and data augmentation to enhance the model's robustness.

4. Training Infrastructure

  • GPU: Training the deep learning model is resource-intensive, and a GPU is recommended for faster training. NVIDIA GPUs with CUDA support are preferred.

5. Model Deployment

  • Flask: The Flask web framework is used for deploying the trained model as a web service. It provides a simple and efficient way to serve predictions.

6. Additional Libraries

  • NumPy: NumPy is used for numerical operations and array manipulations, especially during data processing and model evaluation.
  • Matplotlib: Matplotlib is used for visualizing training/validation curves, performance metrics, and sample predictions.

Setup Instructions

  1. Clone the repository: 
    https://github.com/SHAMSUNDAR20/FACE_IDENTIFICATION_USING-_DEEPLEARNING.git
cd FACE_IDENTIFICATION_USING-_DEEPLEARNING