Paper Info
Title
PCA and LDA based face recognition using feedforward neural network classifier
Abstract
Principal component analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are among the most common feature extraction techniques used for the recognition of faces. In this paper, two face recognition systems, one based on the PCA followed by a feedforward neural network (FFNN) called PCA-NN, and the other based on LDA followed by a FFNN called LDA-NN, are developed. The two systems consist of two phases which are the PCA or LDA preprocessing phase, and the neural network classification phase. The proposed systems show improvement on the recognition rates over the conventional LDA and PCA face recognition systems that use Euclidean Distance based classifier. Additionally, the recognition performance of LDA-NN is higher than the PCA-NN among the proposed systems.
Year
DOI
Venue
2006
10.1007/11848035_28
Lecture Notes in Computer Science
Keywords
Field
DocType
feedforward neural network,lda preprocessing phase,conventional lda,face recognition system,pca face recognition system,recognition rate,proposed system,neural network classification phase,euclidean distance,recognition performance,feedforward neural network classifier,pattern recognition,content analysis,discriminant analysis,neural network,principal component analysis,classification,image processing,computer security,face recognition,facies,multimedia,feature extraction
Facial recognition system,Feedforward neural network,Pattern recognition,Computer science,Euclidean distance,Speech recognition,Feature extraction,Artificial intelligence,Linear discriminant analysis,Artificial neural network,Classifier (linguistics),Principal component analysis
Conference
Volume
ISSN
ISBN
4105
0302-9743
3-540-39392-7
Citations 
PageRank 
References 
9
0.57
11
Authors
2
Name
Order
Citations
PageRank
Alaa Eleyan1515.64
Hasan Demirel261043.71