What is eigenfaces algorithm?
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 is the underlying principle of the eigenfaces machine learning technique?
The basic idea behind the Eigenfaces algorithm is that face images are “projected” into a low dimensional space in which they can be compared efficiently.
How are eigenfaces used in computer vision problem?
Eigenfaces is the name given to a set of eigenvectors when they are used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face classification.
Are there disadvantages to using eigenfaces for face recognition?
In eigenfaces illumination is also considered an important feature of the face which actually isn’t and due to this some main features are discarded considering them less important. This is a major disadvantage of the eigenfaces algortihm which was later fixed by fisherfaces and LBPH algorithm.
How is the Eigenfaces algorithm implemented in openimaj?
The Eigenfaces algorithm is simple to implement using OpenIMAJ using the EigenImages class. The EigenImages class automatically deals with converting the input images into vectors and zero-centering them (subtracting the mean) before applying PCA.
How are eigenvectors used in automated face recognition?
In addition to designing a system for automated face recognition using eigenfaces, they showed a way of calculating the eigenvectors of a covariance matrix such that computers of the time could perform eigen-decomposition on a large number of face images.