Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

University of Central Florida

Facial Recognition: Eigenvectors and Covariance Matrices - Lecture 14

University of Central Florida via YouTube

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

Reviews

Start your review of Facial Recognition: Eigenvectors and Covariance Matrices - Lecture 14

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.