  In this paper we describe a procedure to reduce the size of the input feature
vector. A complex pattern recognition problem like face recognition involves
huge dimension of input feature vector. To reduce that dimension here we have
used eigenspace projection (also called as Principal Component Analysis), which
is basically transformation of space. To reduce further we have applied feature
selection method to select indispensable features, which will remain in the
final feature vectors. Features those are not selected are removed from the
final feature vector considering them as redundant or superfluous. For
selection of features we have used the concept of reduct and core from rough
set theory. This method has shown very good performance. It is worth to mention
that in some cases the recognition rate increases with the decrease in the
feature vector dimension.
