Eigenfaces for Recognition

Title: Eigenfaces for Recognition
Author: Matthew Turk and Alex Pentland
Year of Publication: 1991

Eigenfaces is a approach that decomposes face images into a small set of characteristic feature images and is based on information theory. In other words, the approach extracts the information contained in a collection of face images and uses the variation of these images to encode and compare individual face images to do face recognition.

In mathematical terms, treat an image as a vector in a very high dimensional space and regard the eigenvectors as a set of features that characterize the variation between face images. By using principal component analysis(PCA), it is possible to construct the subspace of face images, called “face space”, with lower dimension. Each individual face can be represented in terms of linear combination of the vectors which are referred to as “eigenfaces” because of face-like appearance. Simply, there are operations as following.

(1) Collect several face images for each person.
(2) Calculate the eigenfaces with the highest associated eigenvalues.
(3) For each known individual, project their face images onto the “face space”. Choose a threshold that defines the maximum allowable distance from any face class.
(4) Calculate the pattern vector for each new face images and the distances to each know class.
(5) If the input image is near face space, it is recognized. And if it is near a known face class, it is known and added to the original set of similar face images to recalculate the eigenfaces; otherwise, it is unknown and it may be used to a new face class.

Moreover, it is possible to detect motion after filtering and rescaling the input image appropriately. Calculate the orientation of the motion of the head or use simple symmetric operators can benefit the recognition of the face rotation.

In my opinion, the approach applying PCA to reduce the dimension is not very difficult, and it is really a good method to construct the eigenfaces. That is, face recognition is nothing but extract the features of face images and decide whether a face is efficiently, and this approach reaches the goal. However, the variant background, the scale of the input images, or the illumination still affect the recognition result a lot. The training set also has a significant effect for the precision of the recognition. How to decide the tradeoff between remained dimensions and the precision rate is another problem.