@article{(Open Science Index):https://publications.waset.org/pdf/8627,
	  title     = {Face Recognition Using Morphological Shared-weight Neural Networks},
	  author    = {Hossein Sahoolizadeh and  Mahdi Rahimi and  Hamid Dehghani},
	  country	= {},
	  institution	= {},
	  abstract     = {We introduce an algorithm based on the
morphological shared-weight neural network. Being nonlinear and
translation-invariant, the MSNN can be used to create better
generalization during face recognition. Feature extraction is
performed on grayscale images using hit-miss transforms that are
independent of gray-level shifts. The output is then learned by
interacting with the classification process. The feature extraction and
classification networks are trained together, allowing the MSNN to
simultaneously learn feature extraction and classification for a face.
For evaluation, we test for robustness under variations in gray levels
and noise while varying the network-s configuration to optimize
recognition efficiency and processing time. Results show that the
MSNN performs better for grayscale image pattern classification
than ordinary neural networks.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {2},
	  number    = {9},
	  year      = {2008},
	  pages     = {1865 - 1868},
	  ee        = {https://publications.waset.org/pdf/8627},
	  url   	= {https://publications.waset.org/vol/21},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 21, 2008},
	}