ABSTRACT
Facial recognition systems have to deal with a variety of problems
for better accuracy results, such as lighting, obstruction, and pose
variation, which occur when comparing an image to be detected
with a previously identified image. In this context, this work aims
to use a pose alignment technique developed by Gang Pan. Together
with the Iterative Closest Ooint (ICP) and Average Face
Model (AFM) techniques, in order to perform a pose correction,
a beginning of 3D facial models, in fully faces (90â—¦) or separately
rotated, and test the result of this facial alignment with the Principal
Component Analysis (PCA), Linear Discriminant Analysis
(LDA), and Support Vector Machine (SVM) recognition and classification
algorithms related to the Local Binary Pattern (LBP), Discrete
Cosine Transform (DCT), and Gaussian Filter preprocessing techniques.
The classification algorithms will be tested in parallel and
independently counted, where one result will not interfere in any
other case, with the use of identifying which algorithm has the best
accuracy. To perform the tests, a facial, text, infrared and visible
light database was created with frontal images on the left, right,
top, bottom and random face pose, resulting in a population of 90
subjects and approximately 1600 colors.