An efficient human face indexing scheme using eigenfaces

We propose an efficient indexing structure for searching a human face in a large database. In our method, a set of eigenfaces is computed based on the faces in the database. Each face in the database is then ranked according to its projection onto each of the eigenfaces. A query input will be ranked similarly, and the corresponding nearest faces in the ranked position with respect to each of the eigenfaces are selected from the database. These selected faces will then form a small database, namely a condensed database, for face recognition, instead of considering the original large database. In the experiments, the effect of the number of eigenfaces used on the size of the condensed database is investigated. Experimental results show that the size of the condensed database is 35% of the original large database when 25% of the eigenfaces with the largest eigenvalues are selected. The processing time required to generate the condensed database is less than one second.