Variations of eigenface are frequently used as the basis of other face recognition methods. Recognition Demo Page As suggested by the graphic, distinctive characteristics of the entire face are highlighted for use in future authentication. The vast majority of faces can be reconstructed by combining features of approximately 100-125 eigenfaces. The degree of variance from the template, of course, will determine acceptance or rejection. Like all facial recognition technology, eigenface technology is best utilized in well-lit, frontal image capture situations.
Feature analysis is perhaps the most widely utilized facial recognition technology.
LFA utilizes dozens of features from different regions of the face, and also incorporates the relative location of these features. It anticipates that the slight movement of a feature located near one's mouth will be accompanied by relatively similar movement of adjacent features. Since feature analysis is not a global representation of the face, it can accommodate angles up to approximately 25 in the horizontal plane, and approximately 15 in the vertical plane. Of course, a straight-ahead video image from a distance of three feet will be the most accurate. Neural networks employ an algorithm to determine the similarity of the unique global features of live versus enrolled or reference faces, using as much of the facial image as possible.