Machine Learning

How to Perform Face Detection with Deep Learning

Last Updated on August 24, 2020

Face detection is a computer vision problem that involves finding faces in photos.

It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. More recently deep learning methods have achieved state-of-the-art results on standard benchmark face detection datasets. One example is the Multi-task Cascade Convolutional Neural Network, or MTCNN for short.

In this tutorial, you will discover how to perform face detection in Python using classical and deep learning models.

After completing this tutorial, you will know:

  • Face detection is a non-trivial computer vision problem for identifying and localizing faces in images.
  • Face detection can be performed using the classical feature-based cascade classifier using the OpenCV library.
  • State-of-the-art face detection can be achieved using a Multi-task Cascade CNN via the MTCNN library.

Kick-start your project with my new book Deep Learning for Computer Vision, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Nov/2019: Updated for TensorFlow v2.0 and MTCNN v0.1.0.
How to Perform Face Detection With Classical and Deep <a href=To finish reading, please visit source site