Illumination and noise tolerant face recognition based on eigen-phase correlation filter modified by Mexican hat wavelet

2009 ◽  
Vol 38 (3) ◽  
pp. 160-168 ◽  
Author(s):  
Pradipta K. Banerjee ◽  
Asit K. Datta
Keyword(s):  
Face Recognition ◽  
Phase Correlation ◽  
Correlation Filter ◽  
Mexican Hat Wavelet ◽  
Mexican Hat ◽  
Noise Tolerant

Band-pass correlation filter for illumination- and noise-tolerant face recognition

2016 ◽  
Vol 11 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Pradipta K. Banerjee ◽  
Asit K. Datta
Keyword(s):  
Face Recognition ◽  
Correlation Filter ◽  
Band Pass ◽  
Noise Tolerant

A METHOD FOR FACE RECOGNITION USING IMAGE REGISTRATION

2011 ◽  
Vol 20 (07) ◽  
pp. 1419-1439 ◽  
Author(s):  
STEVEN GILLAN ◽  
PANAJOTIS AGATHOKLIS
Keyword(s):  
Face Recognition ◽  
Image Registration ◽  
Zernike Moments ◽  
Feature Points ◽  
Post Processing ◽  
Training Images ◽  
Mexican Hat Wavelet ◽  
Mexican Hat ◽  
The Face ◽  
Transformation Parameters

This paper presents a technique for face recognition that is based on image registration. The face recognition technique consists of three parts: a training part, an image registration part and a post-processing part. The image registration technique is based on finding a set of feature points in the two images and using these feature points for registration. This is done in four steps. In the first, images are filtered with the Mexican-hat wavelet to obtain the feature point locations. In the second, the Zernike moments of neighborhoods around the feature points are calculated and compared in the third step to establish correspondence between feature points in the two images. In the fourth, the transformation parameters between images are obtained using an iterative least squares technique to eliminate outliers.1,2 During training, a set of images are chosen as the training images and the Zernike moments for the feature points of the training images are obtained and stored. The choice of training images depends on the changes of poses and illumination that are expected. In the registration part, the transformation parameters to register the training images with the images under consideration are obtained. In the post-processing, these transformation parameters are used to determine whether a valid match is found or not. The performance of the proposed method is evaluated using various face databases3–5 and it is compared with the performance of existing techniques. Results indicate that the proposed technique gives excellent results for face recognition in conditions of varying pose, illumination, background and scale.

Biometric face recognition using Mexican hat wavelet kernel based SVM

2015 ◽  
Author(s):  
Vikram Panigrahi ◽  
Pradyut Kumar Biswal ◽  
Rahul Bastia ◽  
Sibasankar Sahoo ◽  
Rajesh K. Mishra ◽  
...  
Keyword(s):  
Face Recognition ◽  
Mexican Hat Wavelet ◽  
Mexican Hat

THE BACK-PROJECTION METHOD FOR CONSTRUCTING 3D NON-TENSOR PRODUCT MOTHER WAVELETS AND THE APPLICATION IN IMAGE EDGE DETECTION

2012 ◽  
Vol 10 (03) ◽  
pp. 1250026 ◽  
Author(s):  
LI ZENG ◽  
JIQIANG GUO ◽  
CHENCHENG HUANG
Keyword(s):  
Tensor Product ◽  
Computational Cost ◽  
Three Dimension ◽  
Back Projection ◽  
Mother Wavelet ◽  
3D Images ◽  
Mexican Hat Wavelet ◽  
Mexican Hat ◽  
Directional Wavelets ◽  
Mother Wavelets

In this paper, a non-tensor product method for constructing three-dimension (3D) mother wavelets by back-projecting two dimension (2D) mother wavelets is presented. We have proved that if a 2D mother wavelet satisfies certain conditions, the back-projection of the 2D mother wavelet is a 3D mother wavelet. And the construction instances of 3D Mexican-hat wavelet and 3D Meyer wavelet are given. These examples imply that we can get some new 3D mother wavelets from known 1D or 2D mother wavelets by using back-projecting method. This method inaugurates a new approach for constructing non-tensor product 3D wavelet. In addition, the non-tensor product 3D Mexican-hat wavelet is used for detecting the edge of two 3D images in our experimental section. Compared with the Mallat's maximum wavelet module approach which uses 3D directional wavelets, experimental results show it can obtain better outcome especial for the edge which the orientation is not along the coordinate axis. Furthermore, the edge is more fine, and the computational cost is much smaller. The non-tensor product mother wavelets constructed by using the method of this paper also can be widely used for compression, filtering and denoising of 3D images.

Three-Dimensional Profilometry Based on Mexican Hat Wavelet Transform

2009 ◽  
Vol 29 (1) ◽  
pp. 197-202 ◽  
Author(s):  
周翔 Zhou Xiang ◽  
赵宏 Zhao Hong
Keyword(s):  
Wavelet Transform ◽  
Three Dimensional ◽  
Mexican Hat Wavelet ◽  
Mexican Hat

Foreground Separation Methods for Satellite and Balloon Experiments

2005 ◽  
Vol 201 ◽  
pp. 71-74
Author(s):  
R. Belén Barreiro ◽  
Michael P. Hobson ◽  
Anthony N. Lasenby ◽  
Patricio Vielva ◽  
Enrique Martínez-González ◽  
...  
Keyword(s):  
Point Source ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Mexican Hat Wavelet ◽  
Mexican Hat ◽  
Balloon Experiments ◽  
Combined Technique ◽  
Physical Components ◽  
Planck Satellite

A combined technique using the maximum-entropy method (MEM) and the mexican hat wavelet (MHW) to separate and reconstruct the physical components of the microwave sky is presented. We apply this method to simulated observations by the ESA Planck satellite in small patches of the sky. The reconstructed maps of the CMB and foregrounds are improved as compared to those obtained with MEM on its own. Moreover, more accurate point source catalogues are produced at each observing frequency. This technique may also be extended to deal with other multifrequency CMB experiments, including all-sky data.

Ca2+ Sparks Detection and Classification using Gaussian-Mexican Hat Wavelet

2007 ◽  
Author(s):  
Zhi Zhou ◽  
Yingzi Du ◽  
George G. Rodney ◽  
Martin F. Schneider
Keyword(s):  
Mexican Hat Wavelet ◽  
Mexican Hat
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