  In this article we propose a method for the recognition of faces with
different facial expressions. For recognition we extract feature vectors by
using log-Gabor filters of multiple orientations and scales. Using sliding
window algorithm and variances -based masking these features are extracted at
image regions that are less affected by the changes of facial expressions.
Extracted features are passed to the Principal Component Analysis (PCA) -based
recognition method. The results of face recognition experiments using
expression variant faces showed that the proposed method could achieve higher
recognition accuracy than many other methods. For development and testing we
used facial images from the AR and FERET databases. Using facial photographs of
more than one thousand persons from the FERET database the proposed method
achieved 96.6-98.9% first one recognition rate and 0.2-0.6% Equal Error Rate
(EER).
