scholarly journals A Comparative Analysis of Feed-Forward and Generalized Regression Neural Networks for Face Recognition Using Principal Component Analysis

2012 ◽  
Vol 2 (3) ◽  
pp. 148-154 ◽  
Author(s):  
Amit Kumar ◽  
Mr. Mahesh Singh
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Comparative Analysis ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Generalized Regression Neural Network ◽  
Feed Forward ◽  
Generalized Regression

In this paper we give a comparative analysis of performance of feed forward neural network and generalized regression neural network based face recognition. We use different inner epoch for different input pattern according to their difficulty of recognition. We run our system for different number of training patterns and test the system’s performance in terms of recognition rate and training time. We run our algorithm for face recognition application using Principal Component Analysis and both neural network. PCA is used for feature extraction and the neural network is used as a classifier to identify the faces. We use the ORL database for all the experiments.

An Improved Kernel Principal Component Analysis Based on Chaotic Artificial Fish School Algorithm

2013 ◽  
Vol 655-657 ◽  
pp. 931-935
Author(s):  
Fang Min Hu ◽  
Hui Ya Zhao
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Recognition Rate ◽  
Feature Selection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Local Optimum ◽  
Fish School ◽  
Feature Subspace

The feature extraction is a great important step for face recognition. When all features are extracted and selected for face recognition, it results in poor recognition rate because there are too many irrelevant, redundant and noisy features which also increase the time consumption. Therefore, a good feature selection method is necessary. This problem can be regarded as a combinatorial optimization solution. To overcome this problem, An improved kernel principal component analysis based on chaotic artificial fish school algorithm is proposed. The feature subspace of face pictures is obtained by standard kernel principal component analysis where a better feature subspace is selected by improved chaotic artificial fish school algorithm which based on couple chaotic maps increases the diversity of fish, has better global convergence ability and is not easy to fall into local optimum when facing with complex problems. The experimental results show that the proposed method has significantly improved the performance of conventional kernel principal component analysis.

scholarly journals A Face Recognition System Based on Principal Component Analysis-Wavelet and Support Vector Machines

2019 ◽  
Vol 3 (2) ◽  
pp. 14-20
Author(s):  
Laith R. Fleah ◽  
Shaimaa A. Al-Aubi
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Face Image ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Face Recognition System

Face recognition can represent a key requirement in various types of applications such as human-computer interface, monitoring systems, as well as personal identification. In this paper, design and implement of face recognition system are introduced. In this system, a combination of principal component analysis (PCA) and wavelet feature extraction algorithms with support vector machine (SVM) and K-nearest neighborhood classifier is used. PCA and wavelet transform methods are used to extract features from face image using and identify the image of the face using SVMs classifier as well as the neural network are used as a classifier to compare its results with the proposed system. For a more comprehensive comparison, two face image databases (Yale and ORL) are used to test the performance of the system. Finally, the experimental results show the efficiency and reliability of face recognition system, and the results demonstrate accuracy on two databases indicated that the results enhancement 5% using the SVM classifier with polynomial Kernel function compared to use feedforward neural network classifier.

scholarly journals Face Recognition Based on PCA Algorithm

2013 ◽  
pp. 79-83
Author(s):  
Taranpreet Singh Ruprah
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Histogram Equalization ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
The Face

This paper is proposed the face recognition method using PCA with neural network back error propagation learning algorithm .In this paper a feature is extracted using principal component analysis and then classification by creation of back propagation neural network. We run our algorithm for face recognition application using principal component analysis, neural network and also calculate its performance by using the photometric normalization technique: Histogram Equalization and comparing with Euclidean Distance, and Normalized correlation classifiers. The system produces promising results for face verification and face recognition. Demonstrate the recognition accuracy for given number of input pattern.

Sign in / Sign up

Export Citation Format

Share Document