This subset selection is accomplished using an object-oriented database to search through the face image annotations. Photobook then presents the user with the top matches found in the database. At any time the user can select a face from among those presented, and Photobook will then use the eigenvector description of that face to sort the entire set of faces in terms of their similarity to the selected face. Photobook then re-presents the user with the face images, now sorted by similarity to the selected face. The figure below shows the typical results of Photobook similarity search using the eigenvector descriptors.
Similarity decreases left to right, top to bottom.
The entire searching and sorting operation takes less than one second on a standard Sun Sparcstation, because each face is described using only a very small number of eigenvector coefficients. Of particular importance is the ability to find the same person despite wide variations in expression and variations such as presence of eye glasses, etc. If the most-similar face was of the same person then a correct recognition was scored. The eigenface technique is easily extended to the description and coding of facial features, yielding eigeneyes, eigennoses and eigenmouths. Eye-movement studies indicate that these particular facial features represent important landmarks for fixation, especially in an attentive discrimination task.