Gabor and Log-Gabor Wavelet for Face Recognition

2010 ◽  
pp. 62-81
Author(s):  
M. Ashraful Amin ◽  
M. Ashraful Amin ◽  
Hong Yan ◽  
Hong Yan
Keyword(s):  
Face Recognition ◽  
Multiple Scales ◽  
Gabor Filter ◽  
Gabor Filters ◽  
Gabor Wavelet ◽  
Feature Vectors ◽  
Gabor Feature ◽  
Log Gabor Wavelet ◽  
Discriminating Ability ◽  
Log Gabor

In practice Gabor wavelet is often applied to extract relevant features from a facial image. This wavelet is constructed using filters of multiple scales and orientations. Based on Gabor’s theory of communication, two methods are proposed to acquire initial features from 2D images that are Gabor wavelet and Log-Gabor wavelet. Theoretically the main difference between these two wavelets is Log-Gabor wavelet produces DC free filter responses, whereas Gabor filter responses retain DC components. This experimental study determines the characteristics of Gabor and Log-Gabor filters for face recognition. In the experiment, two sixth order data tensor are created; one containing the basic Gabor feature vectors and the other containing the basic Log-Gabor feature vectors. This study reveals the characteristics of the filter orientations for Gabor and Log-Gabor filters for face recognition. These two implementations show that the Gabor filter having orientation zero means oriented at 0 degree with respect to the aligned face has the highest discriminating ability, while Log-Gabor filter with orientation three means 45 degree has the highest discriminating ability. This result is consistent across three different frequencies (scales) used for this experiment. It is also observed that for both the wavelets, filters with low frequency have higher discriminating ability.

AN EMPIRICAL STUDY ON THE CHARACTERISTICS OF GABOR REPRESENTATIONS FOR FACE RECOGNITION

2009 ◽  
Vol 23 (03) ◽  
pp. 401-431 ◽  
Author(s):  
M. ASHRAFUL AMIN ◽  
HONG YAN
Keyword(s):  
Face Recognition ◽  
Feature Vector ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Basic Feature ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Feature Representations ◽  
Gabor Feature

This paper examines the classification capability of different Gabor representations for human face recognition. Usually, Gabor filter responses for eight orientations and five scales for each orientation are calculated and all 40 basic feature vectors are concatenated to assemble the Gabor feature vector. This work explores 70 different Gabor feature vector extraction techniques for face recognition. The main goal is to determine the characteristics of the 40 basic Gabor feature vectors and to devise a faster Gabor feature extraction method. Among all the 40 basic Gabor feature representations the filter responses acquired from the largest scale at smallest relative orientation change (with respect to face) shows the highest discriminating ability for face recognition while classification is performed using three classification methods: probabilistic neural networks (PNN), support vector machines (SVM) and decision trees (DT). A 40 times faster summation based Gabor representation shows about 98% recognition rate while classification is performed using SVM. In this representation all 40 basic Gabor feature vectors are summed to form the summation based Gabor feature vector. In the experiment, a sixth order data tensor containing the basic Gabor feature vectors is constructed, for all the operations.

Content Based Image Retrieval Based on Log Gabor Wavelet Transform

2011 ◽  
Vol 403-408 ◽  
pp. 871-878 ◽  
Author(s):  
Megha Agarwal ◽  
Rudra Prakash Maheshwari
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Retrieval System ◽  
Gabor Wavelet ◽  
Content Based Image Retrieval ◽  
Natural Image ◽  
Feature Descriptor ◽  
Gabor Wavelet Transform ◽  
Log Gabor Wavelet ◽  
Log Gabor

This paper proposes a novel approach of content based image retrieval based on Log Gabor Wavelet Transform (LGWT). It is observed that LGWT better represents an image compared to Gabor Wavelet Transform (GWT). Experimental results illustrate the comparative analysis of proposed retrieval system and the retrieval system based on GWT feature descriptor. It is verified that LGWT based retrieval system improves the average precision and average recall (55.46% and 32.03% respectively) from GWT based retrieval system (50.61% and 31.63% respectively). All the experiments are performed on Corel 1000 natural image database.

Mean shift based log-Gabor wavelet image coding

2007 ◽  
Vol 8 (4) ◽  
pp. 620-624
Author(s):  
Ji-liang Li ◽  
Xiang-zhong Fang ◽  
Jun Hou
Keyword(s):  
Image Coding ◽  
Mean Shift ◽  
Gabor Wavelet ◽  
Wavelet Image Coding ◽  
Log Gabor Wavelet ◽  
Wavelet Image ◽  
Log Gabor

Image Displacement Detection under Low Illumination Using Joint Transform Correlator with Wavelet Denoising

2011 ◽  
Vol 128-129 ◽  
pp. 602-606
Author(s):  
Qi Li ◽  
Peng Ge ◽  
Hua Jun Feng ◽  
Zhi Hai Xu
Keyword(s):  
Detection Method ◽  
Wavelet Denoising ◽  
Gabor Wavelet ◽  
Joint Transform Correlator ◽  
Target Image ◽  
Low Contrast ◽  
Image Displacement ◽  
Low Illumination ◽  
Log Gabor Wavelet ◽  
Log Gabor

Since joint transform correlator (JTC) cannot directly detect the displacement between reference and target images without adequate exposure, an image displacement detection method using JTC based on log-Gabor wavelet denoising is proposed. The method uses a log-Gabor wavelet transform to denoise the reference and the target image obtained in the condition lack of enough exposure, preserving the phase information of them. Results show that the method can successfully accomplish the motion detection, RMSE of displacement measurement using JTC with wavelet denoising could be within 0.3 pixels under 1/80 of normal exposure. The method improved the detection ability of JTC in the condition of low illumination and low contrast, and has great application prospect under these circumstances.

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