Mohamed Fakir
Sultan Moulay Slimane University

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Recognition of a Face in a Mixed Document Lhoussaine Bouhou; Rachid El Ayachi; Mohamed Fakir; Mohamed Oukessou
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp301-312

Abstract

Face recognition is the field of great interest in the domaine of research for several applications such as biometry identification, surveillance, and human-machine interaction…This paper exposes a system of face recognition. This system exploits an image document text embedding a color human face image. Initially, the system, in its phase of extraction, exploitis the horizontal and vertical histogram of the document, detects the image which contains the human face. The second task of the system consists of detecting the included face in other to determine, with the help of invariants moments, the characteristics of the face. The third and last task of the system is to determine, via the same invariants moments, the characteristics of each face stored in a database in order to compare them by means of a classification tool (Neural Networks and K nearest neighbors) with the one determined in the second task for the purpose of taking the decision of identification in that database, of the most similar face to the one detected in the input image.
Handwritten tifinagh character recognition using simple geometric shapes and graphs Youssef Ouadid; Abderrahmane Elbalaoui; Mehdi Boutaounte; Mohamed Fakir; Brahim Minaoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp598-605

Abstract

In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.