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Articles by M. Madadpour Inallou
Total Records ( 2 ) for M. Madadpour Inallou
  Seyyed Mohammad Reza Hashemi , Mahdi Saadati , Mohsen Haji Ghorbani and M. Madadpour Inallou
  The necessity to use the new technologies and carrying out more detailed studies based on identification of best methods in order to detect people’s face with respect to modes and position angles in the image, prompted the researchers to further focus on this issue. What issue in the event of face detection is always intended as a question mark of full knowledge and ingenuity is to achieve results in line with targeting of scientific and security agencies. Therefore with increasing the risks of terrorism and the necessity to identify the suspect people in doing such actions, carrying out a comprehensive study in order to collect the most new face detection techniques is inevitable. In this study, we studied and compared five modern methods in face detection that include IWC-F, Eignfaces, Fuzzy-IWO, FLDA-PCA, Fuzzy-Ga, Fisherface. We evaluated these methods on the same terms and with the ORL standard dataset and presented the results in detail. In this study, we find that the Fisherface algorithm has better results than other algorithms in the presented database.
  Mohammad Mahdi Deramgozin , Seyyed Mohammad Reza Hashemi , Mohsen Hajighorbani and M. Madadpour Inallou
  In this study, face recognition in angled status using invasive weed optimization algorithm is studied. Invasive weed optimization algorithm is one of the most recent optimization algorithms in evolutionary algorithms family. Combined with other existing algorithms, invasive weed optimization algorithm proposes an appropriate approach in order to an improved solution. In this study, a combination of fuzzy cost function and invasive weed optimization algorithm is used in order to human face recognition. Fuzzy logic is used in order to face recognition in angled status and invasive weed algorithm is used in order to obtain the optimal threshold to find the favourable solution. Tests conducted on the proposed algorithm shows 91.4% of existing faces in MIT database.
 
 
 
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