Abstract: Facial expression is complex in nature due to legion of variations present. These variations are identified and recorded using feature extraction mechanisms. The researchers have worked towards it and created classifiers for identifying face expression. The classifiers involve Principal component analysis (PCA), Local Polynomial approximation (LPA), Linear binary pattern (LBP), Discrete wavelet transformation (DWT) etc. The proposed work deals with the new classifier using SIFT key with genetic algorithm to identify distinct facial expression. Optimal features of existing algorithms are used within the proposed work. Also comparison of existing techniques such as LBP, PCA and DWT is presented with SIFT key with genetic algorithm. The results show that proposed classifier gives better result in terms of recognition raet.

Taqdir and Renu Dhir, “Face Recognition using SIFT Key with Optimal Features Selection Model” International Journal of Advanced Computer Science and Applications(IJACSA), 8(2), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080251

@article{2017,
title = {Face Recognition using SIFT Key with Optimal Features Selection Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080251},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080251},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {2},
author = {Taqdir and Renu Dhir}
}