Abdelfatah Aref Tamimi

Work place: Faculty of Sciences & IT, Al-Zaytoonah University of Jordan, Dept. of Computer Science, P.O. Box130, Amman (11733), Jordan

E-mail: drtamimi@zuj.edu.jo


Research Interests: Information Systems, Information Retrieval, Multimedia Information System, Information Theory


Dr. Abdelfatah A. Tamimi has been a member of the Faculty of Science and Information Technology at Al-Zaytoonah University since 1996, where he held different positions including the Dean of the Faculty and the Chair of the Department of Computer Science. He received his Ph.D. in computer science from the City University of New York, NY, USA; his Master’s degree in computer science from Montclair State University, NJ, USA; and his Bachelor’s degree in mathematics from Jordan University, Amman, Jordan. In addition to his research and teaching experience, he has a 15 year experience in information technology design, development and implementation in United States companies.

Author Articles
Real-Time Group Face-Detection for an Intelligent Class-Attendance System

By Abdelfatah Aref Tamimi Omaima N. A. AL-Allaf Mohammad A. Alia

DOI: https://doi.org/10.5815/ijitcs.2015.06.09, Pub. Date: 8 May 2015

The traditional manual attendance system wastes time over students’ responses, but it has worked well for small numbers of students. This research presents a real-time group face-detection system. This system will be used later for student class attendance through automatic student identification. The system architecture and its algorithm will be described in details. The algorithm for the system was based on analyzing facial properties and features in order to perform face detection for checking students’ attendance in real time. The classroom’s camera captures the students’ photo. Then, face detection will be implemented automatically to generate a list of detected student faces. Many experiments were adopted based on real time video captured using digital cameras. The experimental results showed that our approach of face detection offers real-time processing speed with good acceptable detection ratio equal to 94.73%.

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