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Updated in [July 27th, 2023]
This course, Deep Learning in Practice III: Face Recognition, is designed to provide learners with a comprehensive introduction to face recognition with deep learning. Anis Koubaa, the instructor, will guide learners through the entire process of face recognition systems, from extracting the face from an image and localizing the face in an image by its bounding box, to processing the extracted face through a convolutional neural network, called FaceNet in this course, to create a fingerprint of the face, which is referred to as face embedding. Learners will also learn how to develop a Python application that performs the abovementioned operations. This course is essential due to the importance of face recognition systems in real-world applications. Prerequisites for this course include basic knowledge of Python programming and a basic understanding of deep learning and TensorFlow. Upon completion of this course, learners will have a thorough understanding of the entire loop of face recognition systems and be able to develop their own applications and integrate them into their projects.
Course Syllabus
Introduction
Face Recognition: Concepts and Theoretical Background
Hands-on I: Create a face embedding using FaceNet in TF Keras
Hands-on II: Recognize a face in an image
Develop your face recognition system
