Evaluation of Feature Extraction Techniques using Neural Network as a Classifier : A Comparative Review for face Recognition

Authors

  • Vinodpuri Rampuri Gosavi  IEEE Student Member, G. S. Mandal’s, M.I.T, Aurangabad Maharashtra, India
  • Dr. G. S. Sable  Professor,, G. S. Mandal’s, M.I.T, Aurangabad Maharashtra, India
  • Dr. Anil K. Deshmane  Professor and Principal J.S.P.M.’s B.I.T., Barshi, Solapur, Maharashtra, India

Keywords:

Face Recognition, Feature Extraction Techniques, Artificial Neural Networks, Accuracy

Abstract

Face recognition has a wide range of possible applications from person identification and surveillance to electronic marketing and advertising for selected customers. In facial recognition, there are different steps such as preprocessing, feature extraction and classification where feature extraction and classification are used to obtain maximum accuracy. In this research paper, different feature extraction techniques such as ASM, AAM, Gabor features, Template based, and several are critically reviewed. Apart from these, the different types of neural classification networks such as convolutional, backpropagation, radial basis function etc. in the domain of facial recognition are explored. The methods and algorithms developed in the current literature are studied and it is revealed that all the techniques are unique and have optimal performance. This research further makes a comparative analysis of these techniques based on their advantages and limitations.

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International Journal of Scientific Research in Science and Technology

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Published

2018-02-28

Issue

Volume 4, Issue 2, January-February-2018

Section

Research Articles

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How to Cite

[1]
Vinodpuri Rampuri Gosavi, Dr. G. S. Sable, Dr. Anil K. Deshmane, " Evaluation of Feature Extraction Techniques using Neural Network as a Classifier : A Comparative Review for face Recognition, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 2, pp.1082-1091, January-February-2018.