Build Jamie – A Facial Recognition AI [2017]
Build and train your own facial recognition AI using python.
- 2.4 Hours of Video
- Certificate of Completion
- Source Code
- Closed Captions
Benefits
- On-demand, 24/7 access
- 2.4 hours of video
- Certificate of completion
- Source code and PDF notes
- Closed captions
Overview
This course has been archived and is no longer supported. For our latest courses on computer vision, consider trying our evergreen curriculums with a 1-Year Unlimited Access Package.
In this course we’ll be building a facial recognition Artificial Intelligence (AI). The approach we’ll take is to train our AI using an existing dataset of faces created by the University of Yale, and then give it our own faces to see if it can recognize them.
The big topics that will be covered in this course center around the concept of Dimensionality Reduction. We’ll cover techniques such as Eigenfaces and Fisherfaces to make our AI detect faces. This will be complemented by a more accurate technique named Local Binary Patterns. We’ll cover the Yale Faces Database and how you can add additional faces to it.
Frameworks and tools covered: Python 2.7 (updated for Python 3.7), Anaconda 5.2, NumPy 1.15, OpenCV 3.4.2, OpenCV-Python 3.4.2, Pillow 5.3.0
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Lessons
Requirements
This course builds up on the concepts covered in our course Build Lorenzo - A Face Swapping AI.
Python 3 is used in this course.
It is recommended to take this course as part of the Python Computer Vision Mini-Degree, which includes 8 courses on the fundamentals of coding with Python, and teaches core concepts in computer vision.
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