TY - JFULL AU - Hossein Sahoolizadeh and Mahdi Rahimi and Hamid Dehghani PY - 2008/10/ TI - Face Recognition Using Morphological Shared-weight Neural Networks T2 - International Journal of Electrical and Computer Engineering SP - 1864 EP - 1868 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/8627 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 21, 2008 N2 - 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. ER -