Deep Learning in Practice III: Face Recognition - CouponED
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Deep Learning in Practice III: Face Recognition


Deep Learning in Practice III: Face Recognition
Get started with face recognition using MTCNN and FaceNet with Tensorflow and Keras New Rating: 0.0 out of 50.0 (0 ratings) 22 students

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What you'll learn

  • Recognize the fundamentals of face recognition systems
  • Extract a face using MTCNN in Python
  • Create the face embedding using FaceNet in Tensorflow and Keras


  • Be familiar with Python programming language.


About the course

Welcome to the course on Deep Learning in Practice III on Face Recognition. I am Anis Koubaa, and I will be your instructor in this course.

This course is the third course in the series Deep Learning in Practice. It provides a fast and easy-to-follow introduction to face recognition with deep learning using MTCNN for face extraction and FaceNet for face recognition. My two previous courses deal with object classification and transfer learning with Tensorflow and Keras.

In this course, you will learn the whole loop of face recognition systems, which starts by extracting the face from an image and localize the face in an image by its bounding box, then we process the extracted face through a convolutional neural network, called FaceNet in our case, to create a fingerprint of the face, which we call face embedding. The face embedding can be stored in a database so that they are compared with other face embeddings to identify the person of interest.

In this course, you will have a step-by-step introduction to this whole loop and I will show you how you can develop a Python application that performs the aforementioned operations. Exciting, right?

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