Howtosignalprocessing
- How does eigenface algorithm work?
- What do you understand by Eigen faces?
- How do you calculate eigenfaces?
- How does LBPH algorithm work?
How does eigenface algorithm work?
Eigenfaces is a method that is useful for face recognition and detection by determining the variance of faces in a collection of face images and use those variances to encode and decode a face in a machine learning way without the full information reducing computation and space complexity.
What do you understand by Eigen faces?
An eigenface (/ˈaɪɡənˌfeɪs/) is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification.
How do you calculate eigenfaces?
6.-Calculate eigenfaces
Each eigenvector is multiplied by the whole normalized training set matrix (the 55225×16 matrix) and as a result, we will have the same amount of eigenfaces as images in our training set.
How does LBPH algorithm work?
Given the above-mentioned parameters, LBPH works as follows; A data set is created by taking images with a camera or taking images that are saved, and then provisioning a unique identifier or name of the person in the image and then adding the images to a database.
