Facial Recognition Project

Prep Time:

4-5 hours

Cook Time:

Serves:

15+ years

Level:

Advanced

About the Recipe

Learn the fundamentals of computer vision and facial recognition.
Understand how to integrate and use various Python libraries for image processing.
Develop skills in real-time image processing and machine learning model integration.

Computer with internet access
Python programming environment set up (Anaconda distribution recommended)
Access to a text editor or an Integrated Development Environment (IDE) like Jupyter Notebook
OpenCV Python library
Dlib Python library
Pre-trained facial recognition models (instructions for download included in steps)
Web camera or a dataset of facial images for testing

Introduction to Facial Recognition:
- Explain the basics of facial recognition technology and its applications.
- Introduce the OpenCV library and its capabilities for image processing.
Setting Up the Environment:
- Install OpenCV and necessary Python libraries for image processing.
- Set up a webcam to capture real-time video input.
Building the Facial Recognition System:
- Guide students through coding a facial detection algorithm using OpenCV.
- Implement a face detection cascade and configure parameters for detection.
Adding Recognition Features:
- Extend the project by adding facial recognition capabilities using pre-trained models or custom training datasets.
- Discuss the training process and accuracy considerations.
Testing and Evaluation:
- Test the facial recognition system with different faces and lighting conditions.
- Evaluate its performance in detecting and recognizing faces accurately.
Reflection and Discussion:
- Reflect on the challenges and ethical considerations of facial recognition technology.
- Discuss real-world applications and implications of facial recognition in society.