Exercise 4. The Eigenfaces method training 1. Aim: acquire skills ...

• scale the calculated eigenvectors to the pixel value range and save them to
pEF array. When the training is finished, the images from the array are
saved automatically to c:\RT\test\eigenfaces\Eigen. The conclusions drawn
from these images should be listed in the report.
• perform the recognition tests for various numbers of dimensions of the face
space. It is necessary to perform the training once (e.g. for 500 dimensions)
and then set the dimensionality using the CutEigen function in the
TrainEF.sf script. An optimal number of dimensions should be found.
Perform the training for the FeretB database and the recognition tests for
the FeretA database for 10, 20, ..., 200 dimensions. Draw the conclusions.
5. Remarks concerning OpenCV:
• there is a CvMat object which represents a matrix. It must be created using
the cvCreateMat function (it returns a pointer to the new object) and
deleted using the cvReleaseMat function. It is recommended to use cvmSet
and cvmGet functions to access the matrix data.
• the calculations should be performed for float data type (CV_MAT32F
matrix type).
• the precision coefficient eps should be set to 10 -3 .
• some examples are presented in Lab.cpp.
• more details can be found in the documentation (chapter 23, opencv.pdf
file).