This repository contains a Jupyter notebook that demonstrates a basic face detection and recognition pipeline using the MTCNN (Multi-task Cascaded Convolutional Networks) model. The notebook is designed to run on Google Colab, making it easy to use without requiring local setup.

The project involves the following steps:

1. Mounting Google Drive: Accessing the dataset stored in Google Drive.
2. Installing MTCNN: Installing the MTCNN library using pip.
3. Importing Libraries: Importing necessary libraries like cv2, os, numpy, tensorflow, and matplotlib.
4. Loading an Image: Loading an image from Google Drive using OpenCV.
5. Converting Image Color: Converting the image from BGR to RGB format for display.
6. Face Detection: Using the MTCNN model to detect faces in the image and highlighting the detected face with a red rectangle.
7. Displaying the Image: Displaying the image with the detected face using matplotlib.

• Python: The primary programming language used for the project.
• Google Colab: Used for running the Jupyter notebook in the cloud.
• Google Drive: Used for storing and accessing the dataset.
• OpenCV: Used for image processing tasks.
• MTCNN: A deep learning model used for face detection.
• TensorFlow: Used as the backend for the MTCNN model.
• Keras-FaceNet: A pre-trained model for generating face embeddings.
• scikit-learn: Used for machine learning tasks such as clustering, classification, or evaluation metrics.
• NumPy: Used for numerical computations and array manipulations.
• Matplotlib: Used for displaying images.

1. Setup: The notebook starts by mounting Google Drive and installing the MTCNN library.
2. Image Loading: An image is loaded from Google Drive using OpenCV.
3. Color Conversion: The image is converted from BGR to RGB format for proper display.
4. Face Detection: The MTCNN model is used to detect faces in the image. The detected face is highlighted with a red rectangle.
5. Display: The image with the detected face is displayed using matplotlib.

• Google Colab: No local setup is required if using Colab.
• Google Drive Account: For storing and accessing the dataset.
• Python Libraries: The notebook will automatically install the required libraries (opencv-python, mtcnn, tensorflow, matplotlib) when run in Colab.