Arsalane Zarghili

Work place: Sidi Mohamed Ben Abdellah University, FSTF, Morocco

E-mail: a.zarghili@ieee.ma

Website:

Research Interests: Information Theory, Information Systems, Distributed Computing, Information Security, Computer Science & Information Technology

Biography

Arsalane Zarghili is a Doctor of Science from Sidi Mohamed Ben Abdellah University (Fez-Morocco). He received his Ph.D. in 2001 and joined the same University in 2002 as Professor at the computer science department of the Faculty of Science and Technology (FST). In 2007 he was head of the computer sciences department and chair of the Software Quality Master in the FST-Fez. He lectures Programming, Distributed, compilation and Information processing, for both undergraduate and master levels. In 2008 he obtained his HDR in information processing. In 2011, he is the co-founder and the head of the Laboratory of Intelligent Systems and Applications in the FST of Fez. He is a member of the steering committee of the department of computer sciences and was a member of the faculty board. In 2011 he is the chair of Master Intelligent Systems and Networks. He is also IEEE member since 2011. His main research is about pattern recognition, image indexing and retrieval systems in cultural heritage, biometric, etc.

Author Articles
Learning a Backpropagation Neural Network With Error Function Based on Bhattacharyya Distance for Face Recognition

By Naouar Belghini Arsalane Zarghili Jamal Kharroubi Aicha Majda

DOI: https://doi.org/10.5815/ijigsp.2012.08.02, Pub. Date: 8 Aug. 2012

In this paper, a color face recognition system is developed to identify human faces using Back propagation neural network. The architecture we adopt is All-Class-in-One-Network, where all the classes are placed in a single network. To accelerate the learning process we propose the use of Bhattacharyya distance as total error to train the network. In the experimental section we compare how the algorithm converge using the mean square error and the Bhattacharyya distance. Experimental results indicated that the image faces can be recognized by the proposed system effectively and swiftly.

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