Skip to content

The Math of Face Recognition

This Stack supports teachers in guiding students to explore the statistical and mathematical principles behind how AI systems recognize and classify faces. Designed to align with high school statistics standards, the lessons introduce concepts such as pixel-based data, decision boundaries, and error thresholds (e.g., mean squared error) in the context of facial recognition technologies like FaceID. Teachers can use these activities to deepen students’ understanding of key math concepts while also prompting critical discussion about the limitations and implications of AI models in real-world applications.

  • AI & Math
  • 4 lessons
  • Created by Victoria Delaney
We need your help to improve these resources. If you would like to help us, please fill out this form and we will reach out with follow up questions.
Image classification FACEID
  • 60 minutes
  • AI & Math

How does FaceID classify images?

In this lesson, students explore facial recognition technology as a “classification task.” Each time a smartphone uses facial recognition, it asks, “Is this me or not me?” Building on lesson 1, where students learned that images are numerical representations of pixel values, this lesson helps students visualize images in multidimensional space, with dimensions equal to the number of facial variables or features in the model.

  • Created by Victoria Delaney
Math of Face ID
  • 60 minutes
  • AI & Math

How does FaceID recognize images?

This lesson introduces high school students to facial recognition and machine learning in statistics. Students will explore how images serve as quantitative data and learn about facial recognition through image comparison, similarity, and error thresholds.

Comic of two students discussing whether BERT and ERNIE are LLMs or Muppets
  • 75 minutes
  • About AI

Are BERT and ERNIE really AI?

In this lesson, students will gain a general overview of various Large Language Models (LLMs), their functions, and their applications in everyday life. By the end of the lesson, they will be able to explain what an LLM is, recognize its uses, and identify some of the popular LLMs currently available.

  • Created by 'Joba Adisa
  • 50 minutes
  • AI & Society

How does algorithmic bias impact different AI applications?

In this lesson, students start to understand the possible consequences of algorithmic bias. They consider a range of applications for which algorithmic bias presents a problem. This could be the 2nd lesson of a 2 part sequence.

  • Created by Parth Sarin and Jacob Wolf
  • Adapted by Chris Mah
© Stanford University.   Stanford, California 94305.