A Comparative Analysis of Feed-Forward and Elman Neural Networks for Face Recognition Using Principal Component Analysis
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 4
Abstract
Abstract—In this paper we give a comparative analysis of performance of feed forward neural network and elman 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. Here 150 face images from the database are taken and some performance metrics such as recognition rate and total training time are calculated. We use two way cross validation approach while calculating recognition rate and total training time . In two way cross validation, we interchange training set into test set and test set into training set. Feed forward neural network has better performance in terms of recognition rate and total training time as compare to elman neural network.Abstract—In this paper we give a comparative analysis of performance of feed forward neural network and elman 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. Here 150 face images from the database are taken and some performance metrics such as recognition rate and total training time are calculated. We use two way cross validation approach while calculating recognition rate and total training time . In two way cross validation, we interchange training set into test set and test set into training set. Feed forward neural network has better performance in terms of recognition rate and total training time as compare to elman neural network.Abstract—In this paper we give a comparative analysis of performance of feed forward neural network and elman 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. Here 150 face images from the database are taken and some performance metrics such as recognition rate and total training time are calculated. We use two way cross validation approach while calculating recognition rate and total training time . In two way cross validation, we interchange training set into test set and test set into training set. Feed forward neural network has better performance in terms of recognition rate and total training time as compare to elman neural network.Abstract—In this paper we give a comparative analysis of performance of feed forward neural network and elman 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. Here 150 face images from the database are taken and some performance metrics such as recognition rate and total training time are calculated. We use two way cross validation approach while calculating recognition rate and total training time . In two way cross validation, we interchange training set into test set and test set into training set. Feed forward neural network has better performance in terms of recognition rate and total training time as compare to elman neural network.
Authors and Affiliations
Amit Kumar1 , Mahesh Singh
Keywords
Related Articles
- EP ID EP87829
- DOI -
- Views 128
- Downloads 0
End to End Delay Improvement Using Multipath Routing in MANET
Congestion and the loss of energy is the most common problem in the Ad hoc network. A considerable amount of energy is consumed in wireless interfaces. On the other hand the actual available bandwidth varies due to...
CONTENT BASED IMAGE RETRIVAL USING FRACTAL SIGNATURE ANALYSIS
Content-based image retrieval has become a reliable tool for digital image applications, especially for image retrieval purposes. There are several advantages of the image retrieval techniques compared to other simple re...
Wavelength Assignment In WDM Optical Networks
This paper analyses the wavelength assignment problem in optical WDM networks. First, the random wavelength assignment algorithm is compared with the first-fit wavelength assignment algorithm, then the sparse wavelength...
GClustering Algorithm
—Graph clustering poses significant challenges because of the complex structures which may be present in the underlying data. The massive size of the underlying graph makes explicit structural enumeration very diff...
Traffic Estimation in Mobile Ad Hoc Network Using Probe Packets
This paper presents a new method for estimating of traffic in mobile ad hoc by using probe packets. Probing based approaches involves sending test transactions over the network to monitor the path and performance o...