Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2006-05-07
Computer Science
Computer Vision and Pattern Recognition
Unpublished manuscript. June 2005. 20 pages, 8 figures, 5 tables
Scientific paper
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).
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