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Facial Recognition: Eigenvectors and Covariance Matrices - Lecture 14

Offered By: University of Central Florida via YouTube

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Facial Recognition Courses Machine Learning Courses Computer Vision Courses Linear Algebra Courses Image Processing Courses Pattern Recognition Courses Eigenvalues Courses Eigenvectors Courses Biometrics Courses

Course Description

Overview

Explore facial recognition technology in this comprehensive lecture from the University of Central Florida. Begin with an introduction to simple approaches and their associated problems before delving into advanced concepts such as eigenvectors and eigenvalues. Examine practical examples and learn how to apply these principles to face recognition systems. Investigate the role of covariance matrices and distance calculations in improving accuracy. Conclude by addressing common challenges in facial recognition and discussing potential solutions to enhance system performance.

Syllabus

Intro
Simple approach
Problems
Eigenvector
Example
Eigenvalues
Eigenvectors
Face Recognition
Covariance Matrix
Distance
Problem
Solution


Taught by

UCF CRCV

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