4.1.2 Computing Features 
The following subsections describe the process used to get the
features that will be independently optimized for each
classifier. The name of the command is
listed for each step. For details on

the arguments and parameter files used, see the
Reference Manual in AppendixB. Section 4.1.3
discusses how the features are optimized for each classifier.
4.1.2.1 Make the Orientation Arrays mkoas
This command reads the fingerprint image files and extracts
the orientation array(oa). This is run on the full set of
finger prints that will be used as the S,trainingsetT^ for the
neuralnetwork

classifier.
4.1.2.2 Make the Covariance Matrix meancov
This command reads a set of oas and computes their sample mean and
sample covariance matrix. 11Is it typically run on the
full set of orientation arrays from

mkoas but could be run on just a reasonably large subset
of the training set.

4.1.2.3 Make the Eigen values and Eigen vectors eva_evt
This program reads the covariance matrix and computes the
eigen values, and the corresponding eigen vectors. The eigen
values are not needed in the training process, but may
be of theoretical

interest. The program calls a sequence of CLAPACK routines[47].
4.1.2.4 Run the Karhunen-Loe`ve Transform lintran
This command applies a specified linear transform to a
set of vectors. The transform matrix is the eigenvectors from

eva_evt.  The set of vectors to which the transform
matrix is being applied is the oas files, from

mkoas, for the training fingerprints. This set of the
resulting low-dimensional Karhunen-Loe`ve(K-L) 
vectors will be used as the training set for the MLP

classifier when optimizing the classifier weights.
A subset of the K-Lvectors will be used as data by

optrws (optimize regional weights command, below) to help optimize
the PNN classifier. Remember this version of
the K-L transform does not subtract the mean vector from

each feature vector. A complete version
of the K-L transform is included in the command kltran.