Data Driven Gabor Wavelet Design for Face Recognition
ABSTRACT:
In this paper we propose a novel data driven strategy for designing Gabor wavelets for face
recognition. Each face image is represented through a multi-sensor scheme, which splits the 2D
frequency plane into a number of channels and identifies the most significant units for
extracting information. The representative units for a set of face images are then derived based
on statistical analysis of these units. The locations of these units in the 2D frequency plane are
then used to design the frequency and orientation of Gabor wavelets for face recognition. Once
frequency and orientation are determined, the scale of a Gabor wavelet is determined by the
sharpness of the filtered images. Two Gabor wavelet based face recognition algorithms are
applied to demonstrate the advantages of the proposed strategy against conventional
parameter settings. Experimental results show that the face recognition algorithms using the
designed Gabor wavelets achieve better performance in terms of accuracy and efficiency. Since
the strategy is based on the training data, it can be easily applied to designing Gabor wavelets
for general pattern recognition task.
Key-Words: Gabor wavelet design; face recognition
INTRODUCTION:
Feature extraction and classifier learning are essential to the performance of a pattern
recognition system. Features extracted should be as discriminative as possible. Classifiers
should be robust enough to handle uncertainty in the data. In this paper, we propose a new
method for designing Gabor wavelets for feature extraction for face recognition. Gabor
features have been widely used in pattern recognition applications such as fingerprint
recognition, character recognition, and texture segmentation. The application of Gabor
wavelets for face recognition has been pioneered by Lades et al.’s work since Dynamic Link
Architecture (DLA) was proposed in 1993. In this system, faces are represented by a rectangular
graph with local features extracted at the nodes using Gabor wavelets, resulting in Gabor jets.
Wiskott extended DLA to Elastic Bunch Graph Matching (EBGM), where graph nodes are
located at a number of facial landmarks. Since then, a large number of elastic graph based
methods have been proposed. All of these methods can be classified as analytic approaches
since the local features extracted from selected facial points are used for recognition. Recently,
Gabor wavelets have also been applied in global form for face recognition. These holistic
methods normally extract Gabor features from the whole face image. Both linear and nonlinear subspace methods are applied thereafter for dimension reduction. A more detailed
review on Gabor wavelet based face recognition methods can be found in.
VEDLABS, #112, Oxford Towers, Old airport Road, Kodihalli, Bangalore-08
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BLOCK DIAGRAM:
HARDWARE AND SOFTWARE REQUIREMENTS:
Software Requirement Specification:

Operating System: Windows XP with SP2

Tool: MATLAB R2010, Version: 7.10.0
Hardware Requirement specification:

Minimum Intel Pentium IV Processor

Primary memory: 2 GB RAM,
REFERENCES:
[1] C. J. Lee and S. D. Wang, "Fingerprint feature extraction using Gabor filters," Electronics
Letters, vol. 35, pp. 288-290, 1999.
[2] X. Wang, X. Ding, and C. Liu, "Gabor filter-based feature extraction for character
recognition," Pattern Recognition, vol. 38, pp. 369-379, 2005.
[3] A. K. Jain and F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters,"
Pattern Recognition, vol. 24, pp. 1167-1186, 1991.
[4] T. P. Weldon, W. E. Higgins, and D. F. Dunn, "Efficient Gabor filter design for texture
segmentation," Pattern Recognition, vol. 29, pp. 2005-2015, 1996.
[5] M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. Von der Malsburg, R. P. Wurtz, and W.
Konen, "Distortion invariant object recognition in the Dynamic Link Architecture," IEEE
Transactions on Computers, vol. 42, pp. 300-311, 1993.
VEDLABS, #112, Oxford Towers, Old airport Road, Kodihalli, Bangalore-08
www.vedlabs.com , Email id: projects@vedlabs.com, Ph: 080-42040494.
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