Structural Feature Based Anomaly Detection for Packed Executable Identification - Computational Intelligence in Security for Information Systems - page 301

A Classifier Ensemble for Face Recognition Using
Gabor Wavelet Features
Hamid Parvin, Nasser Mozayani, and Akram Beigi
School of Computer Engineering, Iran University of Science and Technology
(IUST), Tehran, Iran
{parvin,mozayani,beigi}@iust.ac.ir
Abstract. Gabor wavelet-based methods have been proven that are useful in
many problems including face detection. It has been shown that these features
tackle well facing into image recognition. In image identification, while there is
a number of human faces in a repository of employees, it is aimed to identify
the face of an arrived employee is which one? So the application of gabor
wavelet-based features is reasonable. We propose a weighted majority average
voting classifier ensemble to handle the problem. We show that the proposed
mechanism works well in an employees' repository of our laboratory.
Keywords: Classifier Ensemble, Gabor Wavelet Features, Face Recognition,
Image Processing.
1 Introduction
Gabor wavelet-based methods have been successfully employed in many computer-
vision problems, such as fingerprint enhancement and texture segmentation [10, 11].
Also similar to the human visual system, Gabor wavelets represent the characteristics
of the spatial localities and the orientation selectivity, and are locally optimal in the
space and frequency domains [12]. Therefore, Gabor Wavelets are proper the choice
for image decomposition and representation when the goal is to derive local and
discriminating features [13].
Combinational Classifiers are so versatile in the fields of artificial intelligence. It
has been proved that a single classifier is not able to learn all the problems because of
three reasons:
1. Problem may inherently be multifunctional.
2. From other side, it is possible that a problem is well-defined for a base
classifier which its recognition is very hard problem.
3. And finally, because of the instability of some base classifiers like
Artificial Neural Networks, Decision Trees, and Bayesian Classifier and
so on, the usage of Combinational Classifiers can be inevitable.
There are several methods to combine a number of classifiers in the field of image
processing. Some of the most important are sum/mean and product methods, ordering
(like max or min) methods and voting methods. There is a good coverage over their
comparisons and evaluations in the [1], [2], [3] and [4]. In [5] and [6] it is shown that