scholarly journals Modular Approach for Face Recognition System using Multilevel Haar Wavelet Transform, Improved PCA and Enhanced Back Propagation Neural Network

2013 ◽  
Vol 75 (7) ◽  
pp. 29-36 ◽  
Author(s):  
Prachi Agarwal ◽  
Naveen Prakash
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Back Propagation ◽  
Recognition System ◽  
Haar Wavelet ◽  
Back Propagation Neural Network ◽  
Modular Approach ◽  
Haar Wavelet Transform ◽  
Face Recognition System

scholarly journals DEVELOPMENT OF BIOMETRIC METHODS AND INFORMATION SECURITY TOOLS

2021 ◽  
Vol 2 (336) ◽  
pp. 121-124
Author(s):  
T. Zh. Mazakov ◽  
D. N. Narynbekovna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Future Perspective ◽  
Data Set

Now a day’s security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.

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