A Comparative Analysis of Feed-Forward and Elman Neural Networks for Face Recognition Using Principal Component Analysis 

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

Analysis of local and global techniques for disparity map generation in stereo vision 

The field of computer vision is still open to generate 3D images (depth) using stereo image pairs. Obtaining reliable depth estimation has importance in robotic applications and autonomous systems. Stereo vision is...

A Study on Handwritten Signature Verification Approaches

People are comfortable with pen and papers for authentication and authorization in legal transactions. Due to increasing the amount of handwritten signatures it is very essential that a person offline hand written signat...

QUERY PLANNING FOR CONTINUOUS AGGREGATION QUERIES USING DATA AGGREGATORS

Continuous aggregation queries are used to monitor the changes in data with time varying for online decision making. For contin uous queries low cost and scalable technique s used a network of aggregators. I...

Decision Strategies during vertical handover in heterogeneous networks  

The next generation (4G) wireless networks is envisioned as a union of different access technologies, using terminals with multiple access interfaces and non-real-time or real-time services. Providing the user wi...

Download PDF file
  • EP ID EP87829
  • DOI -
  • Views 155
  • Downloads 0

How To Cite

Amit Kumar1, Mahesh Singh (2012). A Comparative Analysis of Feed-Forward and Elman Neural Networks for Face Recognition Using Principal Component Analysis . International Journal of Advanced Research in Computer Engineering & Technology(IJARCET), 1(4), 238-243. https://europub.co.uk/articles/-A-87829