  Principle Component Analysis PCA is a classical feature extraction and data
representation technique widely used in pattern recognition. It is one of the
most successful techniques in face recognition. But it has drawback of high
computational especially for big size database. This paper conducts a study to
optimize the time complexity of PCA (eigenfaces) that does not affects the
recognition performance. The authors minimize the participated eigenvectors
which consequently decreases the computational time. A comparison is done to
compare the differences between the recognition time in the original algorithm
and in the enhanced algorithm. The performance of the original and the enhanced
proposed algorithm is tested on face94 face database. Experimental results show
that the recognition time is reduced by 35% by applying our proposed enhanced
algorithm. DET Curves are used to illustrate the experimental results.
