scholarly journals Probability Distribution Functions Based Face Recognition System Using Discrete Wavelet Subbands

10.5772/16075 ◽  
2011 ◽  
Author(s):  
Hasan Demirel ◽  
Gholamreza Anbarjafari
Keyword(s):  
Face Recognition ◽  
Probability Distribution ◽  
Recognition System ◽  
Distribution Functions ◽  
Discrete Wavelet ◽  
Probability Distribution Functions ◽  
Face Recognition System

scholarly journals Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 157 ◽  
Author(s):  
Saad Allagwail ◽  
Osman Gedik ◽  
Javad Rahebi
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Gabor Filter ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Training Images ◽  
Face Recognition System ◽  
Training Samples

In the practical reality of face recognition applications, the human face can have only a limited number of training images. However, it is known that, in general, increasing the number of training images also increases the performance of face recognition systems. In this case, a new set of training samples can be generated from the original samples, using the symmetry property of the face. Although many face recognition methods have been proposed in the literature, a robust face recognition system is still a challenging task. In this paper, recognition performance was improved by using the property of face symmetry. Moreover, the effects of illumination and pose variations were reduced. A Two-Dimensional Discrete Wavelet Transform, based on the Local Binary Pattern, which is a new approach for face recognition using symmetry, has been presented. The method has three main stages, preprocessing, feature extraction, and classification. A Two-Dimensional Discrete Wavelet Transform with Single-Level and Gaussian Low-Pass Filter were used, separately, for preprocessing. The Local Binary Pattern, Gray Level Co-Occurrence Matrix, and the Gabor filter were used for feature extraction, and the Euclidean Distance was used for classification. The proposed method was implemented and evaluated using the Olivetti Research Laboratory (ORL) and Yale datasets. This study also examined the importance of the preprocessing stage in a face recognition system. The experimental results showed that the proposed method had a recognition accuracy of 100%, for both the ORL and Yale datasets, and these recognition rates were higher than the methods in the literature.

scholarly journals Face Recognition Using MLP and RBF Neural Network with Gabor and Discrete Wavelet Transform Characterization: A Comparative Study

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Fatma Zohra Chelali ◽  
Amar Djeradi
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Extraction Methods ◽  
Discrete Wavelet ◽  
Face Representation ◽  
Face Recognition System ◽  
Mlp Classifier

Face recognition has received a great attention from a lot of researchers in computer vision, pattern recognition, and human machine computer interfaces in recent years. Designing a face recognition system is a complex task due to the wide variety of illumination, pose, and facial expression. A lot of approaches have been developed to find the optimal space in which face feature descriptors are well distinguished and separated. Face representation using Gabor features and discrete wavelet has attracted considerable attention in computer vision and image processing. We describe in this paper a face recognition system using artificial neural networks like multilayer perceptron (MLP) and radial basis function (RBF) where Gabor and discrete wavelet based feature extraction methods are proposed for the extraction of features from facial images using two facial databases: the ORL and computer vision. Good recognition rate was obtained using Gabor and DWT parameterization with MLP classifier applied for computer vision dataset.

scholarly journals The Using of PCA, Wavelet and GLCM In Face Recognition System, A Comparative Study

2018 ◽  
Vol 26 (10) ◽  
pp. 131-139 ◽  
Author(s):  
Manar Abdulkaream Al-Abaji ◽  
Meaad Mohammed Salih
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Reduction Techniques ◽  
System A ◽  
Face Recognition System ◽  
Occurrence Matrix

The process of data dimension reduction plays an important role in any  face recognition system because many of these data are repetitive and irrelevant and this cause a problem in applications of data mining and learning the machine. The main purpose is to improve the performance of recognition by eliminating repetitive features.           In this research, a number of data reduction techniques were used like: Principal Component Analysis, Gray-Level Co-occurrence Matrix and Discrete Wavelet Transform for extracting the most important features from the images of persons. A different number of training and testing images were used to compare the performance of each of the techniques above in the recognition process. Euclidean distance scale was used to get results.  

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