Situation invariant Face Recognition using Neural Networks
P. Liyakat. International Journal of Trend in Scientific Research and Development, 2 (4):
995-998(June 2018)
Abstract
Here we consider human face as biometric. Original method of feature extraction from image data is introduced using feed forward Neural Network (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by NN a network, and to a system using PCA as a feature extractor and NN network in the role of classifier. Prof. Suryawanshi Rupali Vithalrao | Prof. Kazi Kutubuddin Sayyad Liyakat"Situation invariant Face Recognition using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14162.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14162/situation-invariant-face-recognition-using-neural-networks/prof-suryawanshi-rupali-vithalrao
%0 Journal Article
%1 noauthororeditor
%A Liyakat, Prof. Suryawanshi Rupali Vithalrao Prof. Kazi Kutubuddin Sayyad
%D 2018
%J International Journal of Trend in Scientific Research and Development
%K & Communication EPOCH Eigen Eigenvalue Electronics Engineering NN PCA Vector
%N 4
%P 995-998
%T Situation invariant Face Recognition using Neural Networks
%U http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14162/situation-invariant-face-recognition-using-neural-networks/prof-suryawanshi-rupali-vithalrao
%V 2
%X Here we consider human face as biometric. Original method of feature extraction from image data is introduced using feed forward Neural Network (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by NN a network, and to a system using PCA as a feature extractor and NN network in the role of classifier. Prof. Suryawanshi Rupali Vithalrao | Prof. Kazi Kutubuddin Sayyad Liyakat"Situation invariant Face Recognition using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14162.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14162/situation-invariant-face-recognition-using-neural-networks/prof-suryawanshi-rupali-vithalrao
@article{noauthororeditor,
abstract = {Here we consider human face as biometric. Original method of feature extraction from image data is introduced using feed forward Neural Network (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by NN a network, and to a system using PCA as a feature extractor and NN network in the role of classifier. Prof. Suryawanshi Rupali Vithalrao | Prof. Kazi Kutubuddin Sayyad Liyakat"Situation invariant Face Recognition using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14162.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14162/situation-invariant-face-recognition-using-neural-networks/prof-suryawanshi-rupali-vithalrao
},
added-at = {2018-09-07T08:38:30.000+0200},
author = {Liyakat, Prof. Suryawanshi Rupali Vithalrao Prof. Kazi Kutubuddin Sayyad},
biburl = {https://www.bibsonomy.org/bibtex/276fe588c726ee149a4f45c80335d9b82/ijtsrd},
interhash = {24267d3dafe214187e110cd1effb932d},
intrahash = {76fe588c726ee149a4f45c80335d9b82},
issn = {2456-6470},
journal = {International Journal of Trend in Scientific Research and Development},
keywords = {& Communication EPOCH Eigen Eigenvalue Electronics Engineering NN PCA Vector},
language = {English},
month = {June},
number = 4,
pages = {995-998},
timestamp = {2018-10-02T11:01:27.000+0200},
title = {Situation invariant Face Recognition using Neural Networks
},
url = {http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14162/situation-invariant-face-recognition-using-neural-networks/prof-suryawanshi-rupali-vithalrao},
volume = 2,
year = 2018
}