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  • 标题:A Comparative Analysis of Feed-Forward and Elman Neural Networks for Face Recognition Using Principal Component Analysis
  • 本地全文:下载
  • 作者:Amit Kumar ; Mahesh Singh
  • 期刊名称:International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
  • 印刷版ISSN:2278-1323
  • 出版年度:2012
  • 卷号:1
  • 期号:4
  • 页码:238-243
  • 出版社:Shri Pannalal Research Institute of Technolgy
  • 摘要: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.
  • 关键词:Feed forward neural network; Elman neural ; network; Principal Component Analysis.
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